{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import itertools\n",
    "\n",
    "import scipy\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram, cut_tree\n",
    "from scipy.spatial.distance import squareform\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import networkx as nx\n",
    "import yfinance as yf\n",
    "\n",
    "from mst import MinimumSpanningTree\n",
    "from utils import compute_log_returns, convert_to_distance_matrix, get_condensed_distance_matrix\n",
    "from graph import build_graph, draw_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [15, 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, I tried to calculate the distance between stocks through the correlation of log-returns and then visualize them through hierarchical clustering(Denodrogram, Minimum Spanning Tree).\n",
    "\n",
    " Next, I de-noised distances that I had calculated above step by using clustering-based filtering. Lastly, I used new distances and visualized stocks as in the previous steps and compared those with the previous results.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loading price data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used [s&p500 list](https://datahub.io/core/s-and-p-500-companies#resource-constituents) and randomly sampled 50 from the list by sector. Then, I already downloaded 1 year(19.3.11~20.3.10) of daily historical prices of them. You can find price data from 'sp50_1year.csv' file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from \"2019-03-11\" to \"2020-03-10\"\n",
    "prices_df = pd.read_csv(\"sp50_1year.csv\", index_col=0)\n",
    "tickers = list(prices_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, You can check information of ticker from 'selected_tickers_info.csv' file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARE</td>\n",
       "      <td>Alexandria Real Estate Equities Inc</td>\n",
       "      <td>Real Estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALLE</td>\n",
       "      <td>Allegion</td>\n",
       "      <td>Industrials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGN</td>\n",
       "      <td>Allergan, Plc</td>\n",
       "      <td>Health Care</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALL</td>\n",
       "      <td>Allstate Corp</td>\n",
       "      <td>Financials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AME</td>\n",
       "      <td>AMETEK Inc.</td>\n",
       "      <td>Industrials</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Symbol                                 Name       Sector\n",
       "0    ARE  Alexandria Real Estate Equities Inc  Real Estate\n",
       "1   ALLE                             Allegion  Industrials\n",
       "2    AGN                        Allergan, Plc  Health Care\n",
       "3    ALL                        Allstate Corp   Financials\n",
       "4    AME                          AMETEK Inc.  Industrials"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticker_info_df = pd.read_csv(\"selected_tickers_info.csv\")\n",
    "ticker_info_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ticker_info_df['Sector'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use sector informations for visualization for later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ARE': 'Real Estate',\n",
       " 'ALLE': 'Industrials',\n",
       " 'AGN': 'Health Care',\n",
       " 'ALL': 'Financials',\n",
       " 'AME': 'Industrials',\n",
       " 'AIV': 'Real Estate',\n",
       " 'ADM': 'Consumer Staples',\n",
       " 'T': 'Telecommunication Services',\n",
       " 'CHRW': 'Industrials',\n",
       " 'CTL': 'Telecommunication Services',\n",
       " 'GLW': 'Information Technology',\n",
       " 'COST': 'Consumer Staples',\n",
       " 'CMI': 'Industrials',\n",
       " 'DVN': 'Energy',\n",
       " 'DFS': 'Financials',\n",
       " 'DISCK': 'Consumer Discretionary',\n",
       " 'DG': 'Consumer Discretionary',\n",
       " 'DTE': 'Utilities',\n",
       " 'EMN': 'Materials',\n",
       " 'EOG': 'Energy',\n",
       " 'ESS': 'Real Estate',\n",
       " 'EL': 'Consumer Staples',\n",
       " 'RE': 'Financials',\n",
       " 'EXPD': 'Industrials',\n",
       " 'FAST': 'Industrials',\n",
       " 'FLIR': 'Information Technology',\n",
       " 'FMC': 'Materials',\n",
       " 'GPS': 'Consumer Discretionary',\n",
       " 'JBHT': 'Industrials',\n",
       " 'JPM': 'Financials',\n",
       " 'LOW': 'Consumer Discretionary',\n",
       " 'MAR': 'Consumer Discretionary',\n",
       " 'MET': 'Financials',\n",
       " 'MU': 'Information Technology',\n",
       " 'NOV': 'Energy',\n",
       " 'NWSA': 'Consumer Discretionary',\n",
       " 'NI': 'Utilities',\n",
       " 'NUE': 'Materials',\n",
       " 'PYPL': 'Information Technology',\n",
       " 'PKI': 'Health Care',\n",
       " 'PRGO': 'Health Care',\n",
       " 'PM': 'Consumer Staples',\n",
       " 'SPGI': 'Financials',\n",
       " 'SWKS': 'Information Technology',\n",
       " 'SLG': 'Real Estate',\n",
       " 'SNA': 'Consumer Discretionary',\n",
       " 'SO': 'Utilities',\n",
       " 'SYF': 'Financials',\n",
       " 'TPR': 'Consumer Discretionary',\n",
       " 'VZ': 'Telecommunication Services'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector_map = ticker_info_df.set_index('Symbol')['Sector'].to_dict()\n",
    "sector_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sector_index = {}\n",
    "for i, sector in enumerate(set(sector_map.values())):\n",
    "    sector_index[sector] = i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can check there are total eleven sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Real Estate': 0,\n",
       " 'Financials': 1,\n",
       " 'Information Technology': 2,\n",
       " 'Utilities': 3,\n",
       " 'Health Care': 4,\n",
       " 'Industrials': 5,\n",
       " 'Energy': 6,\n",
       " 'Telecommunication Services': 7,\n",
       " 'Consumer Staples': 8,\n",
       " 'Consumer Discretionary': 9,\n",
       " 'Materials': 10}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating log returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADM</th>\n",
       "      <th>AGN</th>\n",
       "      <th>AIV</th>\n",
       "      <th>ALL</th>\n",
       "      <th>ALLE</th>\n",
       "      <th>AME</th>\n",
       "      <th>ARE</th>\n",
       "      <th>CHRW</th>\n",
       "      <th>CMI</th>\n",
       "      <th>COST</th>\n",
       "      <th>...</th>\n",
       "      <th>RE</th>\n",
       "      <th>SLG</th>\n",
       "      <th>SNA</th>\n",
       "      <th>SO</th>\n",
       "      <th>SPGI</th>\n",
       "      <th>SWKS</th>\n",
       "      <th>SYF</th>\n",
       "      <th>T</th>\n",
       "      <th>TPR</th>\n",
       "      <th>VZ</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-12</th>\n",
       "      <td>0.001633</td>\n",
       "      <td>0.007609</td>\n",
       "      <td>0.009189</td>\n",
       "      <td>-0.000425</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.005437</td>\n",
       "      <td>-0.001351</td>\n",
       "      <td>0.002402</td>\n",
       "      <td>0.014136</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001376</td>\n",
       "      <td>0.008852</td>\n",
       "      <td>0.002499</td>\n",
       "      <td>0.005433</td>\n",
       "      <td>0.001317</td>\n",
       "      <td>0.009527</td>\n",
       "      <td>0.005916</td>\n",
       "      <td>0.013476</td>\n",
       "      <td>-0.002366</td>\n",
       "      <td>-0.002608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-13</th>\n",
       "      <td>0.008355</td>\n",
       "      <td>0.018174</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>-0.005543</td>\n",
       "      <td>0.009302</td>\n",
       "      <td>0.011784</td>\n",
       "      <td>0.008455</td>\n",
       "      <td>0.003712</td>\n",
       "      <td>-0.009259</td>\n",
       "      <td>0.014574</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>-0.005236</td>\n",
       "      <td>-0.008459</td>\n",
       "      <td>0.000967</td>\n",
       "      <td>0.011078</td>\n",
       "      <td>-0.005975</td>\n",
       "      <td>0.021803</td>\n",
       "      <td>-0.011492</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>0.003997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-14</th>\n",
       "      <td>0.001847</td>\n",
       "      <td>-0.004462</td>\n",
       "      <td>0.008903</td>\n",
       "      <td>0.003202</td>\n",
       "      <td>-0.006736</td>\n",
       "      <td>-0.003713</td>\n",
       "      <td>0.003672</td>\n",
       "      <td>0.002914</td>\n",
       "      <td>0.000955</td>\n",
       "      <td>-0.010804</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003201</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>-0.014642</td>\n",
       "      <td>-0.003874</td>\n",
       "      <td>0.009469</td>\n",
       "      <td>-0.004167</td>\n",
       "      <td>-0.002433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.022701</td>\n",
       "      <td>0.005017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-15</th>\n",
       "      <td>-0.003929</td>\n",
       "      <td>0.009633</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.010493</td>\n",
       "      <td>-0.004064</td>\n",
       "      <td>0.001982</td>\n",
       "      <td>-0.009491</td>\n",
       "      <td>-0.003813</td>\n",
       "      <td>0.001908</td>\n",
       "      <td>-0.001027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>-0.012534</td>\n",
       "      <td>-0.005956</td>\n",
       "      <td>0.006384</td>\n",
       "      <td>0.007807</td>\n",
       "      <td>0.028212</td>\n",
       "      <td>0.014210</td>\n",
       "      <td>0.012797</td>\n",
       "      <td>-0.027567</td>\n",
       "      <td>0.007564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-18</th>\n",
       "      <td>-0.016344</td>\n",
       "      <td>-0.002715</td>\n",
       "      <td>-0.019688</td>\n",
       "      <td>0.001896</td>\n",
       "      <td>0.005527</td>\n",
       "      <td>0.011687</td>\n",
       "      <td>-0.002137</td>\n",
       "      <td>0.012173</td>\n",
       "      <td>0.019752</td>\n",
       "      <td>0.012718</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006099</td>\n",
       "      <td>-0.004991</td>\n",
       "      <td>0.020787</td>\n",
       "      <td>-0.001544</td>\n",
       "      <td>0.012424</td>\n",
       "      <td>-0.014067</td>\n",
       "      <td>-0.002104</td>\n",
       "      <td>0.004230</td>\n",
       "      <td>0.022721</td>\n",
       "      <td>-0.005496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ADM       AGN       AIV       ALL      ALLE       AME  \\\n",
       "Date                                                                     \n",
       "2019-03-12  0.001633  0.007609  0.009189 -0.000425  0.002593  0.000624   \n",
       "2019-03-13  0.008355  0.018174  0.000596 -0.005543  0.009302  0.011784   \n",
       "2019-03-14  0.001847 -0.004462  0.008903  0.003202 -0.006736 -0.003713   \n",
       "2019-03-15 -0.003929  0.009633  0.000197  0.010493 -0.004064  0.001982   \n",
       "2019-03-18 -0.016344 -0.002715 -0.019688  0.001896  0.005527  0.011687   \n",
       "\n",
       "                 ARE      CHRW       CMI      COST  ...        RE       SLG  \\\n",
       "Date                                                ...                       \n",
       "2019-03-12  0.005437 -0.001351  0.002402  0.014136  ...  0.001376  0.008852   \n",
       "2019-03-13  0.008455  0.003712 -0.009259  0.014574  ...  0.000367 -0.005236   \n",
       "2019-03-14  0.003672  0.002914  0.000955 -0.010804  ...  0.003201  0.000984   \n",
       "2019-03-15 -0.009491 -0.003813  0.001908 -0.001027  ...  0.000183 -0.012534   \n",
       "2019-03-18 -0.002137  0.012173  0.019752  0.012718  ...  0.006099 -0.004991   \n",
       "\n",
       "                 SNA        SO      SPGI      SWKS       SYF         T  \\\n",
       "Date                                                                     \n",
       "2019-03-12  0.002499  0.005433  0.001317  0.009527  0.005916  0.013476   \n",
       "2019-03-13 -0.008459  0.000967  0.011078 -0.005975  0.021803 -0.011492   \n",
       "2019-03-14 -0.014642 -0.003874  0.009469 -0.004167 -0.002433  0.000000   \n",
       "2019-03-15 -0.005956  0.006384  0.007807  0.028212  0.014210  0.012797   \n",
       "2019-03-18  0.020787 -0.001544  0.012424 -0.014067 -0.002104  0.004230   \n",
       "\n",
       "                 TPR        VZ  \n",
       "Date                            \n",
       "2019-03-12 -0.002366 -0.002608  \n",
       "2019-03-13  0.002366  0.003997  \n",
       "2019-03-14 -0.022701  0.005017  \n",
       "2019-03-15 -0.027567  0.007564  \n",
       "2019-03-18  0.022721 -0.005496  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_return_df = compute_log_returns(prices_df)\n",
    "log_return_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting correlation to distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get $NxN$ distance matrix by converting correlation matrix to $NxN$ matrix $D$ whose item defined as\n",
    "$d_{i, j} = \\sqrt{2(1-\\rho_{i, j})}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_matrix = log_return_df.corr(method='pearson')\n",
    "distance_matrix = convert_to_distance_matrix(correlation_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hierarchical Clustering Dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hc_single = linkage(squareform(distance_matrix), method=\"single\")\n",
    "ddata = dendrogram(hc_single, labels=distance_matrix.index)\n",
    "plt.xlabel('Tickers', fontsize=12)\n",
    "plt.ylabel('Distances', fontsize=12)\n",
    "plt.title('Hierarchical Clustering Dendrogram', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, distances of sub-clusters are mostly concentrated, we can expect that it is not easy to get beautiful and nice clustering image. You can check details of the dendrogram below dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster_id_1</th>\n",
       "      <th>cluster_id_2</th>\n",
       "      <th>distance</th>\n",
       "      <th>number of members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cluster_1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.346869</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_2</th>\n",
       "      <td>11.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.457362</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_3</th>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.486907</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.509748</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.586114</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_6</th>\n",
       "      <td>51.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.593741</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_7</th>\n",
       "      <td>6.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.642848</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_8</th>\n",
       "      <td>3.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.644946</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_9</th>\n",
       "      <td>17.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.658835</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_10</th>\n",
       "      <td>33.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.662294</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_11</th>\n",
       "      <td>14.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.673057</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_12</th>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.686806</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_13</th>\n",
       "      <td>32.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.712516</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_14</th>\n",
       "      <td>8.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.716076</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_15</th>\n",
       "      <td>23.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.735300</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_16</th>\n",
       "      <td>24.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.752570</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_17</th>\n",
       "      <td>40.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.758673</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_18</th>\n",
       "      <td>56.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.762399</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_19</th>\n",
       "      <td>41.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.763908</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_20</th>\n",
       "      <td>59.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.766929</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_21</th>\n",
       "      <td>42.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.770697</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_22</th>\n",
       "      <td>31.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.777174</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_23</th>\n",
       "      <td>21.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.778260</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_24</th>\n",
       "      <td>44.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.780800</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_25</th>\n",
       "      <td>68.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.789252</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_26</th>\n",
       "      <td>39.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.797388</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_27</th>\n",
       "      <td>36.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.817927</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_29</th>\n",
       "      <td>28.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.840676</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_30</th>\n",
       "      <td>25.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.843731</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_31</th>\n",
       "      <td>20.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.844377</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_32</th>\n",
       "      <td>71.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.845789</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_33</th>\n",
       "      <td>47.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.858548</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_34</th>\n",
       "      <td>35.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.863176</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_35</th>\n",
       "      <td>29.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.866357</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_36</th>\n",
       "      <td>22.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.871159</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_37</th>\n",
       "      <td>82.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.880133</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_38</th>\n",
       "      <td>26.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.882944</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_39</th>\n",
       "      <td>16.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.890307</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_40</th>\n",
       "      <td>4.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_41</th>\n",
       "      <td>79.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.904135</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_42</th>\n",
       "      <td>9.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.911257</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_43</th>\n",
       "      <td>7.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>0.939029</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_44</th>\n",
       "      <td>10.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.982801</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_45</th>\n",
       "      <td>12.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.988594</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_46</th>\n",
       "      <td>13.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.993683</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_47</th>\n",
       "      <td>38.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.029831</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_48</th>\n",
       "      <td>37.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.048691</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_49</th>\n",
       "      <td>1.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cluster_id_1  cluster_id_2  distance  number of members\n",
       "cluster_1           15.0          18.0  0.346869                2.0\n",
       "cluster_2           11.0          46.0  0.457362                2.0\n",
       "cluster_3           27.0          30.0  0.486907                2.0\n",
       "cluster_4            2.0          19.0  0.509748                2.0\n",
       "cluster_5            5.0          52.0  0.586114                3.0\n",
       "cluster_6           51.0          54.0  0.593741                5.0\n",
       "cluster_7            6.0          53.0  0.642848                3.0\n",
       "cluster_8            3.0          55.0  0.644946                6.0\n",
       "cluster_9           17.0          34.0  0.658835                2.0\n",
       "cluster_10          33.0          50.0  0.662294                3.0\n",
       "cluster_11          14.0          43.0  0.673057                2.0\n",
       "cluster_12          57.0          58.0  0.686806                8.0\n",
       "cluster_13          32.0          60.0  0.712516                3.0\n",
       "cluster_14           8.0          61.0  0.716076                9.0\n",
       "cluster_15          23.0          63.0  0.735300               10.0\n",
       "cluster_16          24.0          64.0  0.752570               11.0\n",
       "cluster_17          40.0          65.0  0.758673               12.0\n",
       "cluster_18          56.0          62.0  0.762399                6.0\n",
       "cluster_19          41.0          67.0  0.763908                7.0\n",
       "cluster_20          59.0          66.0  0.766929               15.0\n",
       "cluster_21          42.0          69.0  0.770697               16.0\n",
       "cluster_22          31.0          45.0  0.777174                2.0\n",
       "cluster_23          21.0          70.0  0.778260               17.0\n",
       "cluster_24          44.0          72.0  0.780800               18.0\n",
       "cluster_25          68.0          73.0  0.789252               25.0\n",
       "cluster_26          39.0          74.0  0.797388               26.0\n",
       "cluster_27          36.0          75.0  0.817927               27.0\n",
       "cluster_28           0.0          76.0  0.827996               28.0\n",
       "cluster_29          28.0          77.0  0.840676               29.0\n",
       "cluster_30          25.0          48.0  0.843731                2.0\n",
       "cluster_31          20.0          78.0  0.844377               30.0\n",
       "cluster_32          71.0          80.0  0.845789               32.0\n",
       "cluster_33          47.0          49.0  0.858548                2.0\n",
       "cluster_34          35.0          81.0  0.863176               33.0\n",
       "cluster_35          29.0          83.0  0.866357               34.0\n",
       "cluster_36          22.0          84.0  0.871159               35.0\n",
       "cluster_37          82.0          85.0  0.880133               37.0\n",
       "cluster_38          26.0          86.0  0.882944               38.0\n",
       "cluster_39          16.0          87.0  0.890307               39.0\n",
       "cluster_40           4.0          88.0  0.892054               40.0\n",
       "cluster_41          79.0          89.0  0.904135               42.0\n",
       "cluster_42           9.0          90.0  0.911257               43.0\n",
       "cluster_43           7.0          91.0  0.939029               44.0\n",
       "cluster_44          10.0          92.0  0.982801               45.0\n",
       "cluster_45          12.0          93.0  0.988594               46.0\n",
       "cluster_46          13.0          94.0  0.993683               47.0\n",
       "cluster_47          38.0          95.0  1.029831               48.0\n",
       "cluster_48          37.0          96.0  1.048691               49.0\n",
       "cluster_49           1.0          97.0  1.170525               50.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(hc_single, columns=['cluster_id_1', 'cluster_id_2', 'distance', 'number of members'],\n",
    "             index=[ \"cluster_{}\".format(i+1) for i in range(hc_single.shape[0])]\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building MST based on distance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs =list(itertools.combinations(tickers, 2)) \n",
    "edges = []\n",
    "for a, b in pairs:\n",
    "    edges.append((distance_matrix.loc[a,b], a, b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The building of tree is completed\n"
     ]
    }
   ],
   "source": [
    "mst = MinimumSpanningTree(tickers, edges)\n",
    "tree = mst.build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.3468686073752864, 'DVN', 'EOG'),\n",
       " (0.4573617879068249, 'DFS', 'SYF'),\n",
       " (0.4869069570100967, 'JPM', 'MET'),\n",
       " (0.5097476163635705, 'AIV', 'ESS'),\n",
       " (0.5861135491821754, 'AME', 'MET'),\n",
       " (0.5937408992884916, 'JPM', 'SYF'),\n",
       " (0.6428480632209306, 'AIV', 'ARE'),\n",
       " (0.6449455731797635, 'ALL', 'MET'),\n",
       " (0.6588354056569681, 'EMN', 'NUE'),\n",
       " (0.66229440749994, 'DVN', 'NOV'),\n",
       " (0.6730573788474928, 'DTE', 'SO'),\n",
       " (0.6868064062912486, 'MET', 'NUE'),\n",
       " (0.7125160525850077, 'DTE', 'NI'),\n",
       " (0.7160762289880247, 'CMI', 'EMN'),\n",
       " (0.735299999435005, 'FMC', 'MET'),\n",
       " (0.7525698155383791, 'CMI', 'GLW'),\n",
       " (0.7586727902020749, 'ALL', 'RE'),\n",
       " (0.7623992982242409, 'DTE', 'ESS'),\n",
       " (0.7639082518686938, 'ARE', 'SLG'),\n",
       " (0.7669288291240538, 'DVN', 'JPM'),\n",
       " (0.7706966382766683, 'CMI', 'SNA'),\n",
       " (0.7771742479352638, 'MU', 'SWKS'),\n",
       " (0.7782604114412082, 'FAST', 'NUE'),\n",
       " (0.7808004959763378, 'ALL', 'SPGI'),\n",
       " (0.7892516522896491, 'ALL', 'DTE'),\n",
       " (0.7973884443760831, 'PYPL', 'SPGI'),\n",
       " (0.8179272681821159, 'GLW', 'PKI'),\n",
       " (0.827995891300944, 'ADM', 'MET'),\n",
       " (0.8406755400155544, 'AME', 'LOW'),\n",
       " (0.8437306719072795, 'GPS', 'TPR'),\n",
       " (0.844376542191775, 'CMI', 'EXPD'),\n",
       " (0.8457885234890541, 'EMN', 'SWKS'),\n",
       " (0.8585480731780843, 'T', 'VZ'),\n",
       " (0.8631761380920793, 'MET', 'NWSA'),\n",
       " (0.8663572643555896, 'EMN', 'MAR'),\n",
       " (0.8711585937626197, 'AME', 'FLIR'),\n",
       " (0.8801330776702293, 'MET', 'T'),\n",
       " (0.882943684307642, 'AME', 'JBHT'),\n",
       " (0.8903070778684214, 'AME', 'EL'),\n",
       " (0.8920541229950418, 'ALLE', 'PYPL'),\n",
       " (0.9041348166685346, 'GPS', 'NUE'),\n",
       " (0.9112572714266368, 'COST', 'SPGI'),\n",
       " (0.939029010617892, 'CHRW', 'EXPD'),\n",
       " (0.9828010313327524, 'CTL', 'MET'),\n",
       " (0.9885936196478736, 'COST', 'DG'),\n",
       " (0.9936832233690891, 'DISCK', 'NUE'),\n",
       " (1.0298314922529577, 'NUE', 'PRGO'),\n",
       " (1.0486908792998693, 'PM', 'T'),\n",
       " (1.1705247058990234, 'AGN', 'PRGO')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MST colored by clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's graph our MST tree and color it by cluster that we just got from hierarchical clustering. As we expect, we can confirm clustering results are not great through tree graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_CLUSTER = 11 # the number of sectors\n",
    "cluster_info = cut_tree(hc_single, NUM_CLUSTER)\n",
    "cluster_info = cluster_info.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = build_graph(tickers, tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.get_cmap('nipy_spectral_r', NUM_CLUSTER)\n",
    "draw_graph(G, cluster_info, cmap, label=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MST colored by sector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "what if we draw sample graph with different color based on sector?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ARE': 'Real Estate',\n",
       " 'ALLE': 'Industrials',\n",
       " 'AGN': 'Health Care',\n",
       " 'ALL': 'Financials',\n",
       " 'AME': 'Industrials',\n",
       " 'AIV': 'Real Estate',\n",
       " 'ADM': 'Consumer Staples',\n",
       " 'T': 'Telecommunication Services',\n",
       " 'CHRW': 'Industrials',\n",
       " 'CTL': 'Telecommunication Services',\n",
       " 'GLW': 'Information Technology',\n",
       " 'COST': 'Consumer Staples',\n",
       " 'CMI': 'Industrials',\n",
       " 'DVN': 'Energy',\n",
       " 'DFS': 'Financials',\n",
       " 'DISCK': 'Consumer Discretionary',\n",
       " 'DG': 'Consumer Discretionary',\n",
       " 'DTE': 'Utilities',\n",
       " 'EMN': 'Materials',\n",
       " 'EOG': 'Energy',\n",
       " 'ESS': 'Real Estate',\n",
       " 'EL': 'Consumer Staples',\n",
       " 'RE': 'Financials',\n",
       " 'EXPD': 'Industrials',\n",
       " 'FAST': 'Industrials',\n",
       " 'FLIR': 'Information Technology',\n",
       " 'FMC': 'Materials',\n",
       " 'GPS': 'Consumer Discretionary',\n",
       " 'JBHT': 'Industrials',\n",
       " 'JPM': 'Financials',\n",
       " 'LOW': 'Consumer Discretionary',\n",
       " 'MAR': 'Consumer Discretionary',\n",
       " 'MET': 'Financials',\n",
       " 'MU': 'Information Technology',\n",
       " 'NOV': 'Energy',\n",
       " 'NWSA': 'Consumer Discretionary',\n",
       " 'NI': 'Utilities',\n",
       " 'NUE': 'Materials',\n",
       " 'PYPL': 'Information Technology',\n",
       " 'PKI': 'Health Care',\n",
       " 'PRGO': 'Health Care',\n",
       " 'PM': 'Consumer Staples',\n",
       " 'SPGI': 'Financials',\n",
       " 'SWKS': 'Information Technology',\n",
       " 'SLG': 'Real Estate',\n",
       " 'SNA': 'Consumer Discretionary',\n",
       " 'SO': 'Utilities',\n",
       " 'SYF': 'Financials',\n",
       " 'TPR': 'Consumer Discretionary',\n",
       " 'VZ': 'Telecommunication Services'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Real Estate': 0,\n",
       " 'Financials': 1,\n",
       " 'Information Technology': 2,\n",
       " 'Utilities': 3,\n",
       " 'Health Care': 4,\n",
       " 'Industrials': 5,\n",
       " 'Energy': 6,\n",
       " 'Telecommunication Services': 7,\n",
       " 'Consumer Staples': 8,\n",
       " 'Consumer Discretionary': 9,\n",
       " 'Materials': 10}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sector_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sector_info = []\n",
    "\n",
    "for ticker in tickers:\n",
    "    sector = sector_map[ticker]\n",
    "    sector_num = sector_index[sector]\n",
    "    sector_info.append(sector_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:0: Real Estate: ARE, AIV, ESS, SLG\n",
      "Label:1: Financials: ALL, DFS, RE, JPM, MET, SPGI, SYF\n",
      "Label:2: Information Technology: GLW, FLIR, MU, PYPL, SWKS\n",
      "Label:3: Utilities: DTE, NI, SO\n",
      "Label:4: Health Care: AGN, PKI, PRGO\n",
      "Label:5: Industrials: ALLE, AME, CHRW, CMI, EXPD, FAST, JBHT\n",
      "Label:6: Energy: DVN, EOG, NOV\n",
      "Label:7: Telecommunication Services: T, CTL, VZ\n",
      "Label:8: Consumer Staples: ADM, COST, EL, PM\n",
      "Label:9: Consumer Discretionary: DISCK, DG, GPS, LOW, MAR, NWSA, SNA, TPR\n",
      "Label:10: Materials: EMN, FMC, NUE\n"
     ]
    }
   ],
   "source": [
    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=0, vmax=NUM_CLUSTER-1))\n",
    "plt.colorbar(sm)\n",
    "draw_graph(G, sector_info, cmap, label=False)\n",
    "\n",
    "for sector_name, index in sector_index.items():\n",
    "    print(f\"Label:{index}: {sector_name}: {', '.join(ticker_info_df[ticker_info_df['Sector']==sector_name]['Symbol'])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Our tree graph are good enough to show the assumption that the movements of stocks in the same sector would be similar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use case of MST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can use structure of MTS to de-noise distance matrix. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can filter $D$ by replacing $d_{i,j}$ to $d^{<}_{i, j} = max_{k}${$d_{ik}, d_{kj}$}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " $d^{<}_{i, j}$ is maximum distance among single steps in a path from $i$ to $j$ in a MST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_distance_in_mst(mst, stock_a, stock_b):\n",
    "    \"\"\"\n",
    "    Return distance of path from stock_a to stock_b in a MST\n",
    "    \"\"\"\n",
    "    _, distance = mst.find_path(stock_a, stock_b)\n",
    "    return distance\n",
    "\n",
    "def get_filtered_distance_in_mst(mst, stock_a, stock_b):\n",
    "    \"\"\"\n",
    "    Return maximum distance among single steps in a path from stock_a to stock_b in a MST\n",
    "    \n",
    "    ex:\n",
    "        >>>> get_distance_in_mts(mst, 'ADM', 'AGN')\n",
    "        >>>> 1.170524\n",
    "    \"\"\"\n",
    "    \n",
    "    if stock_a == stock_b:\n",
    "        return 0\n",
    "    else:\n",
    "        path, _ = mst.find_path(stock_a, stock_b)\n",
    "        return max([get_distance_in_mst(mst, i, j) for i, j in zip(path, path[1:])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_ds_matrix = distance_matrix.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance of pair: [ADM, AGN] is changed 1.2579 -> 1.1705\n",
      "Distance of pair: [ADM, AIV] is changed 1.0906 -> 0.8280\n",
      "Distance of pair: [ADM, ALL] is changed 0.8811 -> 0.8280\n",
      "Distance of pair: [ADM, ALLE] is changed 1.0657 -> 0.8921\n",
      "Distance of pair: [ADM, AME] is changed 0.8740 -> 0.8280\n",
      "Distance of pair: [ADM, ARE] is changed 1.0728 -> 0.8280\n",
      "Distance of pair: [ADM, CHRW] is changed 1.1723 -> 0.9390\n",
      "Distance of pair: [ADM, CMI] is changed 0.9173 -> 0.8280\n",
      "Distance of pair: [ADM, COST] is changed 1.0709 -> 0.9113\n",
      "Distance of pair: [ADM, CTL] is changed 1.0851 -> 0.9828\n",
      "Distance of pair: [ADM, DFS] is changed 0.9190 -> 0.8280\n",
      "Distance of pair: [ADM, DG] is changed 1.2100 -> 0.9886\n",
      "Distance of pair: [ADM, DISCK] is changed 1.0530 -> 0.9937\n",
      "Distance of pair: [ADM, DTE] is changed 1.0199 -> 0.8280\n",
      "Distance of pair: [ADM, DVN] is changed 0.9558 -> 0.8280\n",
      "Distance of pair: [ADM, EL] is changed 1.0875 -> 0.8903\n",
      "Distance of pair: [ADM, EMN] is changed 0.8790 -> 0.8280\n",
      "Distance of pair: [ADM, EOG] is changed 0.9894 -> 0.8280\n",
      "Distance of pair: [ADM, ESS] is changed 1.0762 -> 0.8280\n",
      "Distance of pair: [ADM, EXPD] is changed 0.9857 -> 0.8444\n",
      "Distance of pair: [ADM, FAST] is changed 0.9579 -> 0.8280\n",
      "Distance of pair: [ADM, FLIR] is changed 0.9602 -> 0.8712\n",
      "Distance of pair: [ADM, FMC] is changed 0.8953 -> 0.8280\n",
      "Distance of pair: [ADM, GLW] is changed 0.9588 -> 0.8280\n",
      "Distance of pair: [ADM, GPS] is changed 1.0401 -> 0.9041\n",
      "Distance of pair: [ADM, JBHT] is changed 1.0400 -> 0.8829\n",
      "Distance of pair: [ADM, JPM] is changed 0.8331 -> 0.8280\n",
      "Distance of pair: [ADM, LOW] is changed 0.9745 -> 0.8407\n",
      "Distance of pair: [ADM, MAR] is changed 0.9351 -> 0.8664\n",
      "Distance of pair: [ADM, MU] is changed 1.0194 -> 0.8458\n",
      "Distance of pair: [ADM, NI] is changed 1.0714 -> 0.8280\n",
      "Distance of pair: [ADM, NOV] is changed 1.0150 -> 0.8280\n",
      "Distance of pair: [ADM, NUE] is changed 0.8337 -> 0.8280\n",
      "Distance of pair: [ADM, NWSA] is changed 0.9688 -> 0.8632\n",
      "Distance of pair: [ADM, PKI] is changed 0.9948 -> 0.8280\n",
      "Distance of pair: [ADM, PM] is changed 1.1116 -> 1.0487\n",
      "Distance of pair: [ADM, PRGO] is changed 1.1379 -> 1.0298\n",
      "Distance of pair: [ADM, PYPL] is changed 1.0528 -> 0.8280\n",
      "Distance of pair: [ADM, RE] is changed 1.0054 -> 0.8280\n",
      "Distance of pair: [ADM, SLG] is changed 1.0203 -> 0.8280\n",
      "Distance of pair: [ADM, SNA] is changed 0.9703 -> 0.8280\n",
      "Distance of pair: [ADM, SO] is changed 1.1711 -> 0.8280\n",
      "Distance of pair: [ADM, SPGI] is changed 0.9634 -> 0.8280\n",
      "Distance of pair: [ADM, SWKS] is changed 0.9033 -> 0.8458\n",
      "Distance of pair: [ADM, SYF] is changed 0.8649 -> 0.8280\n",
      "Distance of pair: [ADM, T] is changed 1.0218 -> 0.8801\n",
      "Distance of pair: [ADM, TPR] is changed 1.0517 -> 0.9041\n",
      "Distance of pair: [ADM, VZ] is changed 1.1509 -> 0.8801\n",
      "Distance of pair: [AGN, AIV] is changed 1.3124 -> 1.1705\n",
      "Distance of pair: [AGN, ALL] is changed 1.2570 -> 1.1705\n",
      "Distance of pair: [AGN, ALLE] is changed 1.2489 -> 1.1705\n",
      "Distance of pair: [AGN, AME] is changed 1.2040 -> 1.1705\n",
      "Distance of pair: [AGN, ARE] is changed 1.3503 -> 1.1705\n",
      "Distance of pair: [AGN, CHRW] is changed 1.3278 -> 1.1705\n",
      "Distance of pair: [AGN, CMI] is changed 1.2491 -> 1.1705\n",
      "Distance of pair: [AGN, COST] is changed 1.2540 -> 1.1705\n",
      "Distance of pair: [AGN, CTL] is changed 1.3467 -> 1.1705\n",
      "Distance of pair: [AGN, DFS] is changed 1.2193 -> 1.1705\n",
      "Distance of pair: [AGN, DG] is changed 1.3470 -> 1.1705\n",
      "Distance of pair: [AGN, DISCK] is changed 1.3286 -> 1.1705\n",
      "Distance of pair: [AGN, DTE] is changed 1.3156 -> 1.1705\n",
      "Distance of pair: [AGN, DVN] is changed 1.2600 -> 1.1705\n",
      "Distance of pair: [AGN, EL] is changed 1.2604 -> 1.1705\n",
      "Distance of pair: [AGN, EMN] is changed 1.1980 -> 1.1705\n",
      "Distance of pair: [AGN, EOG] is changed 1.2680 -> 1.1705\n",
      "Distance of pair: [AGN, ESS] is changed 1.3470 -> 1.1705\n",
      "Distance of pair: [AGN, EXPD] is changed 1.2467 -> 1.1705\n",
      "Distance of pair: [AGN, FAST] is changed 1.2540 -> 1.1705\n",
      "Distance of pair: [AGN, FLIR] is changed 1.2507 -> 1.1705\n",
      "Distance of pair: [AGN, FMC] is changed 1.2083 -> 1.1705\n",
      "Distance of pair: [AGN, GLW] is changed 1.2380 -> 1.1705\n",
      "Distance of pair: [AGN, GPS] is changed 1.2997 -> 1.1705\n",
      "Distance of pair: [AGN, JBHT] is changed 1.2362 -> 1.1705\n",
      "Distance of pair: [AGN, JPM] is changed 1.2368 -> 1.1705\n",
      "Distance of pair: [AGN, LOW] is changed 1.2797 -> 1.1705\n",
      "Distance of pair: [AGN, MAR] is changed 1.2853 -> 1.1705\n",
      "Distance of pair: [AGN, MET] is changed 1.2281 -> 1.1705\n",
      "Distance of pair: [AGN, MU] is changed 1.2768 -> 1.1705\n",
      "Distance of pair: [AGN, NI] is changed 1.3021 -> 1.1705\n",
      "Distance of pair: [AGN, NOV] is changed 1.2434 -> 1.1705\n",
      "Distance of pair: [AGN, NUE] is changed 1.1778 -> 1.1705\n",
      "Distance of pair: [AGN, NWSA] is changed 1.2268 -> 1.1705\n",
      "Distance of pair: [AGN, PKI] is changed 1.2299 -> 1.1705\n",
      "Distance of pair: [AGN, PM] is changed 1.2422 -> 1.1705\n",
      "Distance of pair: [AGN, PYPL] is changed 1.2824 -> 1.1705\n",
      "Distance of pair: [AGN, RE] is changed 1.2973 -> 1.1705\n",
      "Distance of pair: [AGN, SLG] is changed 1.2958 -> 1.1705\n",
      "Distance of pair: [AGN, SNA] is changed 1.2033 -> 1.1705\n",
      "Distance of pair: [AGN, SO] is changed 1.3462 -> 1.1705\n",
      "Distance of pair: [AGN, SPGI] is changed 1.2396 -> 1.1705\n",
      "Distance of pair: [AGN, SWKS] is changed 1.2684 -> 1.1705\n",
      "Distance of pair: [AGN, SYF] is changed 1.2281 -> 1.1705\n",
      "Distance of pair: [AGN, T] is changed 1.2533 -> 1.1705\n",
      "Distance of pair: [AGN, TPR] is changed 1.2866 -> 1.1705\n",
      "Distance of pair: [AGN, VZ] is changed 1.3294 -> 1.1705\n",
      "Distance of pair: [AIV, ALL] is changed 0.9407 -> 0.7893\n",
      "Distance of pair: [AIV, ALLE] is changed 1.0514 -> 0.8921\n",
      "Distance of pair: [AIV, AME] is changed 1.0033 -> 0.7893\n",
      "Distance of pair: [AIV, CHRW] is changed 1.2808 -> 0.9390\n",
      "Distance of pair: [AIV, CMI] is changed 1.1797 -> 0.7893\n",
      "Distance of pair: [AIV, COST] is changed 0.9847 -> 0.9113\n",
      "Distance of pair: [AIV, CTL] is changed 1.2059 -> 0.9828\n",
      "Distance of pair: [AIV, DFS] is changed 1.1371 -> 0.7893\n",
      "Distance of pair: [AIV, DG] is changed 1.1785 -> 0.9886\n",
      "Distance of pair: [AIV, DISCK] is changed 1.2301 -> 0.9937\n",
      "Distance of pair: [AIV, DTE] is changed 0.7767 -> 0.7624\n",
      "Distance of pair: [AIV, DVN] is changed 1.1941 -> 0.7893\n",
      "Distance of pair: [AIV, EL] is changed 1.0913 -> 0.8903\n",
      "Distance of pair: [AIV, EMN] is changed 1.1916 -> 0.7893\n",
      "Distance of pair: [AIV, EOG] is changed 1.1939 -> 0.7893\n",
      "Distance of pair: [AIV, EXPD] is changed 1.1990 -> 0.8444\n",
      "Distance of pair: [AIV, FAST] is changed 1.1258 -> 0.7893\n",
      "Distance of pair: [AIV, FLIR] is changed 1.0007 -> 0.8712\n",
      "Distance of pair: [AIV, FMC] is changed 1.0915 -> 0.7893\n",
      "Distance of pair: [AIV, GLW] is changed 1.1607 -> 0.7893\n",
      "Distance of pair: [AIV, GPS] is changed 1.2869 -> 0.9041\n",
      "Distance of pair: [AIV, JBHT] is changed 1.1638 -> 0.8829\n",
      "Distance of pair: [AIV, JPM] is changed 1.0842 -> 0.7893\n",
      "Distance of pair: [AIV, LOW] is changed 1.0758 -> 0.8407\n",
      "Distance of pair: [AIV, MAR] is changed 1.2604 -> 0.8664\n",
      "Distance of pair: [AIV, MET] is changed 1.0637 -> 0.7893\n",
      "Distance of pair: [AIV, MU] is changed 1.2765 -> 0.8458\n",
      "Distance of pair: [AIV, NI] is changed 0.8965 -> 0.7624\n",
      "Distance of pair: [AIV, NOV] is changed 1.2425 -> 0.7893\n",
      "Distance of pair: [AIV, NUE] is changed 1.1495 -> 0.7893\n",
      "Distance of pair: [AIV, NWSA] is changed 1.1531 -> 0.8632\n",
      "Distance of pair: [AIV, PKI] is changed 1.1399 -> 0.8179\n",
      "Distance of pair: [AIV, PM] is changed 1.0997 -> 1.0487\n",
      "Distance of pair: [AIV, PRGO] is changed 1.2179 -> 1.0298\n",
      "Distance of pair: [AIV, PYPL] is changed 1.0760 -> 0.7974\n",
      "Distance of pair: [AIV, RE] is changed 0.9936 -> 0.7893\n",
      "Distance of pair: [AIV, SLG] is changed 0.8115 -> 0.7639\n",
      "Distance of pair: [AIV, SNA] is changed 1.1409 -> 0.7893\n",
      "Distance of pair: [AIV, SO] is changed 0.8419 -> 0.7624\n",
      "Distance of pair: [AIV, SPGI] is changed 0.9603 -> 0.7893\n",
      "Distance of pair: [AIV, SWKS] is changed 1.2031 -> 0.8458\n",
      "Distance of pair: [AIV, SYF] is changed 1.0858 -> 0.7893\n",
      "Distance of pair: [AIV, T] is changed 1.0881 -> 0.8801\n",
      "Distance of pair: [AIV, TPR] is changed 1.2608 -> 0.9041\n",
      "Distance of pair: [AIV, VZ] is changed 1.1126 -> 0.8801\n",
      "Distance of pair: [ALL, ALLE] is changed 0.9681 -> 0.8921\n",
      "Distance of pair: [ALL, AME] is changed 0.7512 -> 0.6449\n",
      "Distance of pair: [ALL, ARE] is changed 0.9421 -> 0.7893\n",
      "Distance of pair: [ALL, CHRW] is changed 1.0952 -> 0.9390\n",
      "Distance of pair: [ALL, CMI] is changed 0.9087 -> 0.7161\n",
      "Distance of pair: [ALL, COST] is changed 0.9401 -> 0.9113\n",
      "Distance of pair: [ALL, CTL] is changed 1.0721 -> 0.9828\n",
      "Distance of pair: [ALL, DFS] is changed 0.8521 -> 0.6449\n",
      "Distance of pair: [ALL, DG] is changed 1.1504 -> 0.9886\n",
      "Distance of pair: [ALL, DISCK] is changed 1.1092 -> 0.9937\n",
      "Distance of pair: [ALL, DVN] is changed 0.9275 -> 0.7669\n",
      "Distance of pair: [ALL, EL] is changed 0.9937 -> 0.8903\n",
      "Distance of pair: [ALL, EMN] is changed 0.9137 -> 0.6868\n",
      "Distance of pair: [ALL, EOG] is changed 0.9616 -> 0.7669\n",
      "Distance of pair: [ALL, ESS] is changed 0.9531 -> 0.7893\n",
      "Distance of pair: [ALL, EXPD] is changed 1.0046 -> 0.8444\n",
      "Distance of pair: [ALL, FAST] is changed 0.9711 -> 0.7783\n",
      "Distance of pair: [ALL, FLIR] is changed 0.9804 -> 0.8712\n",
      "Distance of pair: [ALL, FMC] is changed 0.8930 -> 0.7353\n",
      "Distance of pair: [ALL, GLW] is changed 0.9926 -> 0.7526\n",
      "Distance of pair: [ALL, GPS] is changed 1.0935 -> 0.9041\n",
      "Distance of pair: [ALL, JBHT] is changed 1.0400 -> 0.8829\n",
      "Distance of pair: [ALL, JPM] is changed 0.7297 -> 0.6449\n",
      "Distance of pair: [ALL, LOW] is changed 0.9518 -> 0.8407\n",
      "Distance of pair: [ALL, MAR] is changed 1.0306 -> 0.8664\n",
      "Distance of pair: [ALL, MU] is changed 1.1073 -> 0.8458\n",
      "Distance of pair: [ALL, NI] is changed 0.9543 -> 0.7893\n",
      "Distance of pair: [ALL, NOV] is changed 0.9416 -> 0.7669\n",
      "Distance of pair: [ALL, NUE] is changed 0.9079 -> 0.6868\n",
      "Distance of pair: [ALL, NWSA] is changed 0.9502 -> 0.8632\n",
      "Distance of pair: [ALL, PKI] is changed 1.0191 -> 0.8179\n",
      "Distance of pair: [ALL, PM] is changed 1.0978 -> 1.0487\n",
      "Distance of pair: [ALL, PRGO] is changed 1.1469 -> 1.0298\n",
      "Distance of pair: [ALL, PYPL] is changed 0.9837 -> 0.7974\n",
      "Distance of pair: [ALL, SLG] is changed 0.9240 -> 0.7893\n",
      "Distance of pair: [ALL, SNA] is changed 0.9506 -> 0.7707\n",
      "Distance of pair: [ALL, SO] is changed 0.9589 -> 0.7893\n",
      "Distance of pair: [ALL, SWKS] is changed 1.0071 -> 0.8458\n",
      "Distance of pair: [ALL, SYF] is changed 0.8220 -> 0.6449\n",
      "Distance of pair: [ALL, T] is changed 0.9148 -> 0.8801\n",
      "Distance of pair: [ALL, TPR] is changed 1.0622 -> 0.9041\n",
      "Distance of pair: [ALL, VZ] is changed 1.0473 -> 0.8801\n",
      "Distance of pair: [ALLE, AME] is changed 0.8945 -> 0.8921\n",
      "Distance of pair: [ALLE, ARE] is changed 0.9754 -> 0.8921\n",
      "Distance of pair: [ALLE, CHRW] is changed 1.2400 -> 0.9390\n",
      "Distance of pair: [ALLE, CMI] is changed 0.9870 -> 0.8921\n",
      "Distance of pair: [ALLE, COST] is changed 1.0249 -> 0.9113\n",
      "Distance of pair: [ALLE, CTL] is changed 1.2058 -> 0.9828\n",
      "Distance of pair: [ALLE, DFS] is changed 0.9964 -> 0.8921\n",
      "Distance of pair: [ALLE, DG] is changed 1.1459 -> 0.9886\n",
      "Distance of pair: [ALLE, DISCK] is changed 1.2337 -> 0.9937\n",
      "Distance of pair: [ALLE, DTE] is changed 1.0736 -> 0.8921\n",
      "Distance of pair: [ALLE, DVN] is changed 1.1322 -> 0.8921\n",
      "Distance of pair: [ALLE, EL] is changed 1.0727 -> 0.8921\n",
      "Distance of pair: [ALLE, EMN] is changed 0.9666 -> 0.8921\n",
      "Distance of pair: [ALLE, EOG] is changed 1.1588 -> 0.8921\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance of pair: [ALLE, ESS] is changed 1.0964 -> 0.8921\n",
      "Distance of pair: [ALLE, EXPD] is changed 1.0684 -> 0.8921\n",
      "Distance of pair: [ALLE, FAST] is changed 1.0074 -> 0.8921\n",
      "Distance of pair: [ALLE, FLIR] is changed 0.9954 -> 0.8921\n",
      "Distance of pair: [ALLE, FMC] is changed 0.9830 -> 0.8921\n",
      "Distance of pair: [ALLE, GLW] is changed 1.0306 -> 0.8921\n",
      "Distance of pair: [ALLE, GPS] is changed 1.1542 -> 0.9041\n",
      "Distance of pair: [ALLE, JBHT] is changed 1.0999 -> 0.8921\n",
      "Distance of pair: [ALLE, JPM] is changed 0.9201 -> 0.8921\n",
      "Distance of pair: [ALLE, LOW] is changed 1.0563 -> 0.8921\n",
      "Distance of pair: [ALLE, MAR] is changed 1.0527 -> 0.8921\n",
      "Distance of pair: [ALLE, MET] is changed 0.9510 -> 0.8921\n",
      "Distance of pair: [ALLE, MU] is changed 1.0899 -> 0.8921\n",
      "Distance of pair: [ALLE, NI] is changed 1.1342 -> 0.8921\n",
      "Distance of pair: [ALLE, NOV] is changed 1.1490 -> 0.8921\n",
      "Distance of pair: [ALLE, NUE] is changed 0.9861 -> 0.8921\n",
      "Distance of pair: [ALLE, NWSA] is changed 1.0940 -> 0.8921\n",
      "Distance of pair: [ALLE, PKI] is changed 1.0773 -> 0.8921\n",
      "Distance of pair: [ALLE, PM] is changed 1.2037 -> 1.0487\n",
      "Distance of pair: [ALLE, PRGO] is changed 1.1092 -> 1.0298\n",
      "Distance of pair: [ALLE, RE] is changed 1.0520 -> 0.8921\n",
      "Distance of pair: [ALLE, SLG] is changed 0.9957 -> 0.8921\n",
      "Distance of pair: [ALLE, SNA] is changed 1.0019 -> 0.8921\n",
      "Distance of pair: [ALLE, SO] is changed 1.1343 -> 0.8921\n",
      "Distance of pair: [ALLE, SPGI] is changed 0.9470 -> 0.8921\n",
      "Distance of pair: [ALLE, SWKS] is changed 1.0380 -> 0.8921\n",
      "Distance of pair: [ALLE, SYF] is changed 0.9609 -> 0.8921\n",
      "Distance of pair: [ALLE, T] is changed 1.1249 -> 0.8921\n",
      "Distance of pair: [ALLE, TPR] is changed 1.1398 -> 0.9041\n",
      "Distance of pair: [ALLE, VZ] is changed 1.1612 -> 0.8921\n",
      "Distance of pair: [AME, ARE] is changed 0.9227 -> 0.7893\n",
      "Distance of pair: [AME, CHRW] is changed 1.0997 -> 0.9390\n",
      "Distance of pair: [AME, CMI] is changed 0.7872 -> 0.7161\n",
      "Distance of pair: [AME, COST] is changed 0.9584 -> 0.9113\n",
      "Distance of pair: [AME, CTL] is changed 1.0461 -> 0.9828\n",
      "Distance of pair: [AME, DFS] is changed 0.7484 -> 0.5937\n",
      "Distance of pair: [AME, DG] is changed 1.1451 -> 0.9886\n",
      "Distance of pair: [AME, DISCK] is changed 1.0710 -> 0.9937\n",
      "Distance of pair: [AME, DTE] is changed 0.9002 -> 0.7893\n",
      "Distance of pair: [AME, DVN] is changed 0.7675 -> 0.7669\n",
      "Distance of pair: [AME, EMN] is changed 0.7337 -> 0.6868\n",
      "Distance of pair: [AME, EOG] is changed 0.7933 -> 0.7669\n",
      "Distance of pair: [AME, ESS] is changed 1.0185 -> 0.7893\n",
      "Distance of pair: [AME, EXPD] is changed 0.9104 -> 0.8444\n",
      "Distance of pair: [AME, FAST] is changed 0.8072 -> 0.7783\n",
      "Distance of pair: [AME, FMC] is changed 0.7463 -> 0.7353\n",
      "Distance of pair: [AME, GLW] is changed 0.8469 -> 0.7526\n",
      "Distance of pair: [AME, GPS] is changed 0.9846 -> 0.9041\n",
      "Distance of pair: [AME, JPM] is changed 0.6385 -> 0.5861\n",
      "Distance of pair: [AME, MAR] is changed 0.9060 -> 0.8664\n",
      "Distance of pair: [AME, MU] is changed 0.9589 -> 0.8458\n",
      "Distance of pair: [AME, NI] is changed 1.0684 -> 0.7893\n",
      "Distance of pair: [AME, NOV] is changed 0.8220 -> 0.7669\n",
      "Distance of pair: [AME, NUE] is changed 0.7341 -> 0.6868\n",
      "Distance of pair: [AME, NWSA] is changed 0.8945 -> 0.8632\n",
      "Distance of pair: [AME, PKI] is changed 0.9266 -> 0.8179\n",
      "Distance of pair: [AME, PM] is changed 1.0966 -> 1.0487\n",
      "Distance of pair: [AME, PRGO] is changed 1.1028 -> 1.0298\n",
      "Distance of pair: [AME, PYPL] is changed 0.8546 -> 0.7974\n",
      "Distance of pair: [AME, RE] is changed 0.9336 -> 0.7587\n",
      "Distance of pair: [AME, SLG] is changed 0.9156 -> 0.7893\n",
      "Distance of pair: [AME, SNA] is changed 0.7961 -> 0.7707\n",
      "Distance of pair: [AME, SO] is changed 1.1198 -> 0.7893\n",
      "Distance of pair: [AME, SPGI] is changed 0.7844 -> 0.7808\n",
      "Distance of pair: [AME, SWKS] is changed 0.8565 -> 0.8458\n",
      "Distance of pair: [AME, SYF] is changed 0.7243 -> 0.5937\n",
      "Distance of pair: [AME, T] is changed 0.9721 -> 0.8801\n",
      "Distance of pair: [AME, TPR] is changed 0.9633 -> 0.9041\n",
      "Distance of pair: [AME, VZ] is changed 1.1670 -> 0.8801\n",
      "Distance of pair: [ARE, CHRW] is changed 1.2826 -> 0.9390\n",
      "Distance of pair: [ARE, CMI] is changed 1.1664 -> 0.7893\n",
      "Distance of pair: [ARE, COST] is changed 1.0600 -> 0.9113\n",
      "Distance of pair: [ARE, CTL] is changed 1.1222 -> 0.9828\n",
      "Distance of pair: [ARE, DFS] is changed 1.0730 -> 0.7893\n",
      "Distance of pair: [ARE, DG] is changed 1.1845 -> 0.9886\n",
      "Distance of pair: [ARE, DISCK] is changed 1.1960 -> 0.9937\n",
      "Distance of pair: [ARE, DTE] is changed 0.8699 -> 0.7624\n",
      "Distance of pair: [ARE, DVN] is changed 1.0911 -> 0.7893\n",
      "Distance of pair: [ARE, EL] is changed 1.0893 -> 0.8903\n",
      "Distance of pair: [ARE, EMN] is changed 1.1112 -> 0.7893\n",
      "Distance of pair: [ARE, EOG] is changed 1.0992 -> 0.7893\n",
      "Distance of pair: [ARE, ESS] is changed 0.7146 -> 0.6428\n",
      "Distance of pair: [ARE, EXPD] is changed 1.1686 -> 0.8444\n",
      "Distance of pair: [ARE, FAST] is changed 1.0803 -> 0.7893\n",
      "Distance of pair: [ARE, FLIR] is changed 1.0271 -> 0.8712\n",
      "Distance of pair: [ARE, FMC] is changed 1.0453 -> 0.7893\n",
      "Distance of pair: [ARE, GLW] is changed 1.1444 -> 0.7893\n",
      "Distance of pair: [ARE, GPS] is changed 1.2107 -> 0.9041\n",
      "Distance of pair: [ARE, JBHT] is changed 1.1057 -> 0.8829\n",
      "Distance of pair: [ARE, JPM] is changed 1.0348 -> 0.7893\n",
      "Distance of pair: [ARE, LOW] is changed 1.0269 -> 0.8407\n",
      "Distance of pair: [ARE, MAR] is changed 1.1892 -> 0.8664\n",
      "Distance of pair: [ARE, MET] is changed 1.0086 -> 0.7893\n",
      "Distance of pair: [ARE, MU] is changed 1.2204 -> 0.8458\n",
      "Distance of pair: [ARE, NI] is changed 0.9446 -> 0.7624\n",
      "Distance of pair: [ARE, NOV] is changed 1.1638 -> 0.7893\n",
      "Distance of pair: [ARE, NUE] is changed 1.0723 -> 0.7893\n",
      "Distance of pair: [ARE, NWSA] is changed 1.1639 -> 0.8632\n",
      "Distance of pair: [ARE, PKI] is changed 1.1323 -> 0.8179\n",
      "Distance of pair: [ARE, PM] is changed 1.1447 -> 1.0487\n",
      "Distance of pair: [ARE, PRGO] is changed 1.2103 -> 1.0298\n",
      "Distance of pair: [ARE, PYPL] is changed 0.9915 -> 0.7974\n",
      "Distance of pair: [ARE, RE] is changed 1.0548 -> 0.7893\n",
      "Distance of pair: [ARE, SNA] is changed 1.0747 -> 0.7893\n",
      "Distance of pair: [ARE, SO] is changed 1.0032 -> 0.7624\n",
      "Distance of pair: [ARE, SPGI] is changed 0.9478 -> 0.7893\n",
      "Distance of pair: [ARE, SWKS] is changed 1.1143 -> 0.8458\n",
      "Distance of pair: [ARE, SYF] is changed 1.0406 -> 0.7893\n",
      "Distance of pair: [ARE, T] is changed 1.1102 -> 0.8801\n",
      "Distance of pair: [ARE, TPR] is changed 1.1943 -> 0.9041\n",
      "Distance of pair: [ARE, VZ] is changed 1.1771 -> 0.8801\n",
      "Distance of pair: [CHRW, CMI] is changed 1.0332 -> 0.9390\n",
      "Distance of pair: [CHRW, COST] is changed 1.2095 -> 0.9390\n",
      "Distance of pair: [CHRW, CTL] is changed 1.2408 -> 0.9828\n",
      "Distance of pair: [CHRW, DFS] is changed 1.1211 -> 0.9390\n",
      "Distance of pair: [CHRW, DG] is changed 1.2756 -> 0.9886\n",
      "Distance of pair: [CHRW, DISCK] is changed 1.1470 -> 0.9937\n",
      "Distance of pair: [CHRW, DTE] is changed 1.2273 -> 0.9390\n",
      "Distance of pair: [CHRW, DVN] is changed 1.1523 -> 0.9390\n",
      "Distance of pair: [CHRW, EL] is changed 1.2288 -> 0.9390\n",
      "Distance of pair: [CHRW, EMN] is changed 1.0714 -> 0.9390\n",
      "Distance of pair: [CHRW, EOG] is changed 1.1592 -> 0.9390\n",
      "Distance of pair: [CHRW, ESS] is changed 1.2810 -> 0.9390\n",
      "Distance of pair: [CHRW, FAST] is changed 1.0607 -> 0.9390\n",
      "Distance of pair: [CHRW, FLIR] is changed 1.1803 -> 0.9390\n",
      "Distance of pair: [CHRW, FMC] is changed 1.2122 -> 0.9390\n",
      "Distance of pair: [CHRW, GLW] is changed 1.1200 -> 0.9390\n",
      "Distance of pair: [CHRW, GPS] is changed 1.1370 -> 0.9390\n",
      "Distance of pair: [CHRW, JBHT] is changed 0.9776 -> 0.9390\n",
      "Distance of pair: [CHRW, JPM] is changed 1.0879 -> 0.9390\n",
      "Distance of pair: [CHRW, LOW] is changed 1.1776 -> 0.9390\n",
      "Distance of pair: [CHRW, MAR] is changed 1.1420 -> 0.9390\n",
      "Distance of pair: [CHRW, MET] is changed 1.0485 -> 0.9390\n",
      "Distance of pair: [CHRW, MU] is changed 1.1730 -> 0.9390\n",
      "Distance of pair: [CHRW, NI] is changed 1.3111 -> 0.9390\n",
      "Distance of pair: [CHRW, NOV] is changed 1.1535 -> 0.9390\n",
      "Distance of pair: [CHRW, NUE] is changed 1.0494 -> 0.9390\n",
      "Distance of pair: [CHRW, NWSA] is changed 1.0764 -> 0.9390\n",
      "Distance of pair: [CHRW, PKI] is changed 1.1623 -> 0.9390\n",
      "Distance of pair: [CHRW, PM] is changed 1.2458 -> 1.0487\n",
      "Distance of pair: [CHRW, PRGO] is changed 1.2104 -> 1.0298\n",
      "Distance of pair: [CHRW, PYPL] is changed 1.2816 -> 0.9390\n",
      "Distance of pair: [CHRW, RE] is changed 1.1673 -> 0.9390\n",
      "Distance of pair: [CHRW, SLG] is changed 1.2757 -> 0.9390\n",
      "Distance of pair: [CHRW, SNA] is changed 1.0467 -> 0.9390\n",
      "Distance of pair: [CHRW, SO] is changed 1.3503 -> 0.9390\n",
      "Distance of pair: [CHRW, SPGI] is changed 1.1616 -> 0.9390\n",
      "Distance of pair: [CHRW, SWKS] is changed 1.1483 -> 0.9390\n",
      "Distance of pair: [CHRW, SYF] is changed 1.1162 -> 0.9390\n",
      "Distance of pair: [CHRW, T] is changed 1.1573 -> 0.9390\n",
      "Distance of pair: [CHRW, TPR] is changed 1.1898 -> 0.9390\n",
      "Distance of pair: [CHRW, VZ] is changed 1.2616 -> 0.9390\n",
      "Distance of pair: [CMI, COST] is changed 1.0599 -> 0.9113\n",
      "Distance of pair: [CMI, CTL] is changed 1.1141 -> 0.9828\n",
      "Distance of pair: [CMI, DFS] is changed 0.8044 -> 0.7161\n",
      "Distance of pair: [CMI, DG] is changed 1.1248 -> 0.9886\n",
      "Distance of pair: [CMI, DISCK] is changed 1.0235 -> 0.9937\n",
      "Distance of pair: [CMI, DTE] is changed 1.1545 -> 0.7893\n",
      "Distance of pair: [CMI, DVN] is changed 1.0166 -> 0.7669\n",
      "Distance of pair: [CMI, EL] is changed 1.0127 -> 0.8903\n",
      "Distance of pair: [CMI, EOG] is changed 1.0162 -> 0.7669\n",
      "Distance of pair: [CMI, ESS] is changed 1.1948 -> 0.7893\n",
      "Distance of pair: [CMI, FAST] is changed 0.7934 -> 0.7783\n",
      "Distance of pair: [CMI, FLIR] is changed 0.9650 -> 0.8712\n",
      "Distance of pair: [CMI, FMC] is changed 0.8957 -> 0.7353\n",
      "Distance of pair: [CMI, GPS] is changed 0.9380 -> 0.9041\n",
      "Distance of pair: [CMI, JBHT] is changed 0.9307 -> 0.8829\n",
      "Distance of pair: [CMI, JPM] is changed 0.7771 -> 0.7161\n",
      "Distance of pair: [CMI, LOW] is changed 0.9736 -> 0.8407\n",
      "Distance of pair: [CMI, MAR] is changed 0.9107 -> 0.8664\n",
      "Distance of pair: [CMI, MET] is changed 0.7549 -> 0.7161\n",
      "Distance of pair: [CMI, MU] is changed 1.0027 -> 0.8458\n",
      "Distance of pair: [CMI, NI] is changed 1.2317 -> 0.7893\n",
      "Distance of pair: [CMI, NOV] is changed 1.0683 -> 0.7669\n",
      "Distance of pair: [CMI, NUE] is changed 0.7256 -> 0.7161\n",
      "Distance of pair: [CMI, NWSA] is changed 0.9321 -> 0.8632\n",
      "Distance of pair: [CMI, PKI] is changed 0.8684 -> 0.8179\n",
      "Distance of pair: [CMI, PM] is changed 1.1529 -> 1.0487\n",
      "Distance of pair: [CMI, PRGO] is changed 1.1264 -> 1.0298\n",
      "Distance of pair: [CMI, PYPL] is changed 1.0520 -> 0.7974\n",
      "Distance of pair: [CMI, RE] is changed 1.0252 -> 0.7587\n",
      "Distance of pair: [CMI, SLG] is changed 1.1088 -> 0.7893\n",
      "Distance of pair: [CMI, SO] is changed 1.2730 -> 0.7893\n",
      "Distance of pair: [CMI, SPGI] is changed 1.0144 -> 0.7808\n",
      "Distance of pair: [CMI, SWKS] is changed 0.8788 -> 0.8458\n",
      "Distance of pair: [CMI, SYF] is changed 0.8267 -> 0.7161\n",
      "Distance of pair: [CMI, T] is changed 0.9842 -> 0.8801\n",
      "Distance of pair: [CMI, TPR] is changed 0.9617 -> 0.9041\n",
      "Distance of pair: [CMI, VZ] is changed 1.1392 -> 0.8801\n",
      "Distance of pair: [COST, CTL] is changed 1.2262 -> 0.9828\n",
      "Distance of pair: [COST, DFS] is changed 1.0226 -> 0.9113\n",
      "Distance of pair: [COST, DISCK] is changed 1.2129 -> 0.9937\n",
      "Distance of pair: [COST, DTE] is changed 0.9983 -> 0.9113\n",
      "Distance of pair: [COST, DVN] is changed 1.2052 -> 0.9113\n",
      "Distance of pair: [COST, EL] is changed 1.0146 -> 0.9113\n",
      "Distance of pair: [COST, EMN] is changed 1.0843 -> 0.9113\n",
      "Distance of pair: [COST, EOG] is changed 1.2239 -> 0.9113\n",
      "Distance of pair: [COST, ESS] is changed 0.9642 -> 0.9113\n",
      "Distance of pair: [COST, EXPD] is changed 1.0644 -> 0.9113\n",
      "Distance of pair: [COST, FAST] is changed 1.1247 -> 0.9113\n",
      "Distance of pair: [COST, FLIR] is changed 1.0213 -> 0.9113\n",
      "Distance of pair: [COST, FMC] is changed 1.0382 -> 0.9113\n",
      "Distance of pair: [COST, GLW] is changed 1.0726 -> 0.9113\n",
      "Distance of pair: [COST, GPS] is changed 1.2167 -> 0.9113\n",
      "Distance of pair: [COST, JBHT] is changed 1.1263 -> 0.9113\n",
      "Distance of pair: [COST, JPM] is changed 1.0152 -> 0.9113\n",
      "Distance of pair: [COST, LOW] is changed 1.0418 -> 0.9113\n",
      "Distance of pair: [COST, MAR] is changed 1.1783 -> 0.9113\n",
      "Distance of pair: [COST, MET] is changed 1.0082 -> 0.9113\n",
      "Distance of pair: [COST, MU] is changed 1.1361 -> 0.9113\n",
      "Distance of pair: [COST, NI] is changed 1.0357 -> 0.9113\n",
      "Distance of pair: [COST, NOV] is changed 1.2069 -> 0.9113\n",
      "Distance of pair: [COST, NUE] is changed 1.0691 -> 0.9113\n",
      "Distance of pair: [COST, NWSA] is changed 1.1230 -> 0.9113\n",
      "Distance of pair: [COST, PKI] is changed 1.1329 -> 0.9113\n",
      "Distance of pair: [COST, PM] is changed 1.1818 -> 1.0487\n",
      "Distance of pair: [COST, PRGO] is changed 1.1592 -> 1.0298\n",
      "Distance of pair: [COST, PYPL] is changed 1.0044 -> 0.9113\n",
      "Distance of pair: [COST, RE] is changed 1.0379 -> 0.9113\n",
      "Distance of pair: [COST, SLG] is changed 0.9946 -> 0.9113\n",
      "Distance of pair: [COST, SNA] is changed 1.1230 -> 0.9113\n",
      "Distance of pair: [COST, SO] is changed 1.0114 -> 0.9113\n",
      "Distance of pair: [COST, SWKS] is changed 1.1194 -> 0.9113\n",
      "Distance of pair: [COST, SYF] is changed 0.9805 -> 0.9113\n",
      "Distance of pair: [COST, T] is changed 1.0042 -> 0.9113\n",
      "Distance of pair: [COST, TPR] is changed 1.1867 -> 0.9113\n",
      "Distance of pair: [COST, VZ] is changed 0.9603 -> 0.9113\n",
      "Distance of pair: [CTL, DFS] is changed 1.0494 -> 0.9828\n",
      "Distance of pair: [CTL, DG] is changed 1.2989 -> 0.9886\n",
      "Distance of pair: [CTL, DISCK] is changed 1.0080 -> 0.9937\n",
      "Distance of pair: [CTL, DTE] is changed 1.1933 -> 0.9828\n",
      "Distance of pair: [CTL, DVN] is changed 1.0433 -> 0.9828\n",
      "Distance of pair: [CTL, EL] is changed 1.1796 -> 0.9828\n",
      "Distance of pair: [CTL, EMN] is changed 1.0464 -> 0.9828\n",
      "Distance of pair: [CTL, EOG] is changed 1.0567 -> 0.9828\n",
      "Distance of pair: [CTL, ESS] is changed 1.1710 -> 0.9828\n",
      "Distance of pair: [CTL, EXPD] is changed 1.2110 -> 0.9828\n",
      "Distance of pair: [CTL, FAST] is changed 1.1286 -> 0.9828\n",
      "Distance of pair: [CTL, FLIR] is changed 1.1575 -> 0.9828\n",
      "Distance of pair: [CTL, FMC] is changed 1.0927 -> 0.9828\n",
      "Distance of pair: [CTL, GLW] is changed 1.0994 -> 0.9828\n",
      "Distance of pair: [CTL, GPS] is changed 1.0818 -> 0.9828\n",
      "Distance of pair: [CTL, JBHT] is changed 1.1592 -> 0.9828\n",
      "Distance of pair: [CTL, JPM] is changed 1.0251 -> 0.9828\n",
      "Distance of pair: [CTL, LOW] is changed 1.0983 -> 0.9828\n",
      "Distance of pair: [CTL, MAR] is changed 1.1173 -> 0.9828\n",
      "Distance of pair: [CTL, MU] is changed 1.1025 -> 0.9828\n",
      "Distance of pair: [CTL, NI] is changed 1.2869 -> 0.9828\n",
      "Distance of pair: [CTL, NOV] is changed 1.0353 -> 0.9828\n",
      "Distance of pair: [CTL, NUE] is changed 1.0237 -> 0.9828\n",
      "Distance of pair: [CTL, NWSA] is changed 1.1603 -> 0.9828\n",
      "Distance of pair: [CTL, PKI] is changed 1.1860 -> 0.9828\n",
      "Distance of pair: [CTL, PM] is changed 1.2301 -> 1.0487\n",
      "Distance of pair: [CTL, PRGO] is changed 1.1345 -> 1.0298\n",
      "Distance of pair: [CTL, PYPL] is changed 1.1984 -> 0.9828\n",
      "Distance of pair: [CTL, RE] is changed 1.2323 -> 0.9828\n",
      "Distance of pair: [CTL, SLG] is changed 1.0955 -> 0.9828\n",
      "Distance of pair: [CTL, SNA] is changed 1.0792 -> 0.9828\n",
      "Distance of pair: [CTL, SO] is changed 1.2835 -> 0.9828\n",
      "Distance of pair: [CTL, SPGI] is changed 1.1492 -> 0.9828\n",
      "Distance of pair: [CTL, SWKS] is changed 1.0710 -> 0.9828\n",
      "Distance of pair: [CTL, SYF] is changed 1.0537 -> 0.9828\n",
      "Distance of pair: [CTL, T] is changed 1.0199 -> 0.9828\n",
      "Distance of pair: [CTL, TPR] is changed 1.0493 -> 0.9828\n",
      "Distance of pair: [CTL, VZ] is changed 1.2137 -> 0.9828\n",
      "Distance of pair: [DFS, DG] is changed 1.1879 -> 0.9886\n",
      "Distance of pair: [DFS, DISCK] is changed 1.0494 -> 0.9937\n",
      "Distance of pair: [DFS, DTE] is changed 1.0145 -> 0.7893\n",
      "Distance of pair: [DFS, DVN] is changed 0.8986 -> 0.7669\n",
      "Distance of pair: [DFS, EL] is changed 0.9797 -> 0.8903\n",
      "Distance of pair: [DFS, EMN] is changed 0.7859 -> 0.6868\n",
      "Distance of pair: [DFS, EOG] is changed 0.9187 -> 0.7669\n",
      "Distance of pair: [DFS, ESS] is changed 1.1060 -> 0.7893\n",
      "Distance of pair: [DFS, EXPD] is changed 0.9528 -> 0.8444\n",
      "Distance of pair: [DFS, FAST] is changed 0.8941 -> 0.7783\n",
      "Distance of pair: [DFS, FLIR] is changed 0.9911 -> 0.8712\n",
      "Distance of pair: [DFS, FMC] is changed 0.8595 -> 0.7353\n",
      "Distance of pair: [DFS, GLW] is changed 0.8868 -> 0.7526\n",
      "Distance of pair: [DFS, GPS] is changed 0.9772 -> 0.9041\n",
      "Distance of pair: [DFS, JBHT] is changed 0.9898 -> 0.8829\n",
      "Distance of pair: [DFS, JPM] is changed 0.6360 -> 0.5937\n",
      "Distance of pair: [DFS, LOW] is changed 0.9453 -> 0.8407\n",
      "Distance of pair: [DFS, MAR] is changed 0.8868 -> 0.8664\n",
      "Distance of pair: [DFS, MET] is changed 0.6626 -> 0.5937\n",
      "Distance of pair: [DFS, MU] is changed 0.9612 -> 0.8458\n",
      "Distance of pair: [DFS, NI] is changed 1.1280 -> 0.7893\n",
      "Distance of pair: [DFS, NOV] is changed 0.9223 -> 0.7669\n",
      "Distance of pair: [DFS, NUE] is changed 0.8075 -> 0.6868\n",
      "Distance of pair: [DFS, NWSA] is changed 0.9113 -> 0.8632\n",
      "Distance of pair: [DFS, PKI] is changed 0.9795 -> 0.8179\n",
      "Distance of pair: [DFS, PM] is changed 1.1265 -> 1.0487\n",
      "Distance of pair: [DFS, PRGO] is changed 1.0646 -> 1.0298\n",
      "Distance of pair: [DFS, PYPL] is changed 0.9650 -> 0.7974\n",
      "Distance of pair: [DFS, RE] is changed 0.9570 -> 0.7587\n",
      "Distance of pair: [DFS, SLG] is changed 0.9738 -> 0.7893\n",
      "Distance of pair: [DFS, SNA] is changed 0.8894 -> 0.7707\n",
      "Distance of pair: [DFS, SO] is changed 1.1807 -> 0.7893\n",
      "Distance of pair: [DFS, SPGI] is changed 0.9253 -> 0.7808\n",
      "Distance of pair: [DFS, SWKS] is changed 0.8683 -> 0.8458\n",
      "Distance of pair: [DFS, T] is changed 0.9501 -> 0.8801\n",
      "Distance of pair: [DFS, TPR] is changed 0.9678 -> 0.9041\n",
      "Distance of pair: [DFS, VZ] is changed 1.1283 -> 0.8801\n",
      "Distance of pair: [DG, DISCK] is changed 1.2995 -> 0.9937\n",
      "Distance of pair: [DG, DTE] is changed 1.1981 -> 0.9886\n",
      "Distance of pair: [DG, DVN] is changed 1.3555 -> 0.9886\n",
      "Distance of pair: [DG, EL] is changed 1.1679 -> 0.9886\n",
      "Distance of pair: [DG, EMN] is changed 1.2048 -> 0.9886\n",
      "Distance of pair: [DG, EOG] is changed 1.3654 -> 0.9886\n",
      "Distance of pair: [DG, ESS] is changed 1.1620 -> 0.9886\n",
      "Distance of pair: [DG, EXPD] is changed 1.1636 -> 0.9886\n",
      "Distance of pair: [DG, FAST] is changed 1.1397 -> 0.9886\n",
      "Distance of pair: [DG, FLIR] is changed 1.1285 -> 0.9886\n",
      "Distance of pair: [DG, FMC] is changed 1.1858 -> 0.9886\n",
      "Distance of pair: [DG, GLW] is changed 1.1792 -> 0.9886\n",
      "Distance of pair: [DG, GPS] is changed 1.2263 -> 0.9886\n",
      "Distance of pair: [DG, JBHT] is changed 1.1789 -> 0.9886\n",
      "Distance of pair: [DG, JPM] is changed 1.1533 -> 0.9886\n",
      "Distance of pair: [DG, LOW] is changed 1.1084 -> 0.9886\n",
      "Distance of pair: [DG, MAR] is changed 1.2172 -> 0.9886\n",
      "Distance of pair: [DG, MET] is changed 1.1777 -> 0.9886\n",
      "Distance of pair: [DG, MU] is changed 1.2396 -> 0.9886\n",
      "Distance of pair: [DG, NI] is changed 1.1739 -> 0.9886\n",
      "Distance of pair: [DG, NOV] is changed 1.2970 -> 0.9886\n",
      "Distance of pair: [DG, NUE] is changed 1.1615 -> 0.9886\n",
      "Distance of pair: [DG, NWSA] is changed 1.2231 -> 0.9886\n",
      "Distance of pair: [DG, PKI] is changed 1.2154 -> 0.9886\n",
      "Distance of pair: [DG, PM] is changed 1.3390 -> 1.0487\n",
      "Distance of pair: [DG, PRGO] is changed 1.2523 -> 1.0298\n",
      "Distance of pair: [DG, PYPL] is changed 1.1658 -> 0.9886\n",
      "Distance of pair: [DG, RE] is changed 1.2039 -> 0.9886\n",
      "Distance of pair: [DG, SLG] is changed 1.2270 -> 0.9886\n",
      "Distance of pair: [DG, SNA] is changed 1.1626 -> 0.9886\n",
      "Distance of pair: [DG, SO] is changed 1.1815 -> 0.9886\n",
      "Distance of pair: [DG, SPGI] is changed 1.1616 -> 0.9886\n",
      "Distance of pair: [DG, SWKS] is changed 1.2083 -> 0.9886\n",
      "Distance of pair: [DG, SYF] is changed 1.1893 -> 0.9886\n",
      "Distance of pair: [DG, T] is changed 1.2170 -> 0.9886\n",
      "Distance of pair: [DG, TPR] is changed 1.2720 -> 0.9886\n",
      "Distance of pair: [DG, VZ] is changed 1.2222 -> 0.9886\n",
      "Distance of pair: [DISCK, DTE] is changed 1.2661 -> 0.9937\n",
      "Distance of pair: [DISCK, DVN] is changed 1.0772 -> 0.9937\n",
      "Distance of pair: [DISCK, EL] is changed 1.1360 -> 0.9937\n",
      "Distance of pair: [DISCK, EMN] is changed 0.9994 -> 0.9937\n",
      "Distance of pair: [DISCK, EOG] is changed 1.0724 -> 0.9937\n",
      "Distance of pair: [DISCK, ESS] is changed 1.2635 -> 0.9937\n",
      "Distance of pair: [DISCK, EXPD] is changed 1.0808 -> 0.9937\n",
      "Distance of pair: [DISCK, FAST] is changed 1.0855 -> 0.9937\n",
      "Distance of pair: [DISCK, FLIR] is changed 1.1047 -> 0.9937\n",
      "Distance of pair: [DISCK, FMC] is changed 1.0973 -> 0.9937\n",
      "Distance of pair: [DISCK, GLW] is changed 1.0219 -> 0.9937\n",
      "Distance of pair: [DISCK, GPS] is changed 1.0114 -> 0.9937\n",
      "Distance of pair: [DISCK, JBHT] is changed 1.1116 -> 0.9937\n",
      "Distance of pair: [DISCK, JPM] is changed 1.0469 -> 0.9937\n",
      "Distance of pair: [DISCK, LOW] is changed 1.0720 -> 0.9937\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance of pair: [DISCK, MAR] is changed 1.0149 -> 0.9937\n",
      "Distance of pair: [DISCK, MET] is changed 1.0064 -> 0.9937\n",
      "Distance of pair: [DISCK, MU] is changed 1.1536 -> 0.9937\n",
      "Distance of pair: [DISCK, NI] is changed 1.3303 -> 0.9937\n",
      "Distance of pair: [DISCK, NOV] is changed 1.1337 -> 0.9937\n",
      "Distance of pair: [DISCK, NWSA] is changed 1.0326 -> 0.9937\n",
      "Distance of pair: [DISCK, PKI] is changed 1.0940 -> 0.9937\n",
      "Distance of pair: [DISCK, PM] is changed 1.2730 -> 1.0487\n",
      "Distance of pair: [DISCK, PRGO] is changed 1.1847 -> 1.0298\n",
      "Distance of pair: [DISCK, PYPL] is changed 1.1817 -> 0.9937\n",
      "Distance of pair: [DISCK, RE] is changed 1.2329 -> 0.9937\n",
      "Distance of pair: [DISCK, SLG] is changed 1.1608 -> 0.9937\n",
      "Distance of pair: [DISCK, SNA] is changed 1.0747 -> 0.9937\n",
      "Distance of pair: [DISCK, SO] is changed 1.3846 -> 0.9937\n",
      "Distance of pair: [DISCK, SPGI] is changed 1.1494 -> 0.9937\n",
      "Distance of pair: [DISCK, SWKS] is changed 1.0692 -> 0.9937\n",
      "Distance of pair: [DISCK, SYF] is changed 1.0910 -> 0.9937\n",
      "Distance of pair: [DISCK, T] is changed 1.1278 -> 0.9937\n",
      "Distance of pair: [DISCK, TPR] is changed 1.0482 -> 0.9937\n",
      "Distance of pair: [DISCK, VZ] is changed 1.2565 -> 0.9937\n",
      "Distance of pair: [DTE, DVN] is changed 1.0214 -> 0.7893\n",
      "Distance of pair: [DTE, EL] is changed 1.0282 -> 0.8903\n",
      "Distance of pair: [DTE, EMN] is changed 1.1156 -> 0.7893\n",
      "Distance of pair: [DTE, EOG] is changed 1.0035 -> 0.7893\n",
      "Distance of pair: [DTE, EXPD] is changed 1.1888 -> 0.8444\n",
      "Distance of pair: [DTE, FAST] is changed 1.1287 -> 0.7893\n",
      "Distance of pair: [DTE, FLIR] is changed 1.0294 -> 0.8712\n",
      "Distance of pair: [DTE, FMC] is changed 1.0124 -> 0.7893\n",
      "Distance of pair: [DTE, GLW] is changed 1.1366 -> 0.7893\n",
      "Distance of pair: [DTE, GPS] is changed 1.2380 -> 0.9041\n",
      "Distance of pair: [DTE, JBHT] is changed 1.1118 -> 0.8829\n",
      "Distance of pair: [DTE, JPM] is changed 0.9140 -> 0.7893\n",
      "Distance of pair: [DTE, LOW] is changed 1.0183 -> 0.8407\n",
      "Distance of pair: [DTE, MAR] is changed 1.1891 -> 0.8664\n",
      "Distance of pair: [DTE, MET] is changed 0.8691 -> 0.7893\n",
      "Distance of pair: [DTE, MU] is changed 1.2261 -> 0.8458\n",
      "Distance of pair: [DTE, NOV] is changed 1.0883 -> 0.7893\n",
      "Distance of pair: [DTE, NUE] is changed 1.0416 -> 0.7893\n",
      "Distance of pair: [DTE, NWSA] is changed 1.0460 -> 0.8632\n",
      "Distance of pair: [DTE, PKI] is changed 1.1242 -> 0.8179\n",
      "Distance of pair: [DTE, PM] is changed 1.0703 -> 1.0487\n",
      "Distance of pair: [DTE, PRGO] is changed 1.1760 -> 1.0298\n",
      "Distance of pair: [DTE, PYPL] is changed 1.0350 -> 0.7974\n",
      "Distance of pair: [DTE, RE] is changed 0.7983 -> 0.7893\n",
      "Distance of pair: [DTE, SLG] is changed 0.8921 -> 0.7639\n",
      "Distance of pair: [DTE, SNA] is changed 1.0941 -> 0.7893\n",
      "Distance of pair: [DTE, SPGI] is changed 0.8666 -> 0.7893\n",
      "Distance of pair: [DTE, SWKS] is changed 1.1826 -> 0.8458\n",
      "Distance of pair: [DTE, SYF] is changed 0.9701 -> 0.7893\n",
      "Distance of pair: [DTE, T] is changed 1.0270 -> 0.8801\n",
      "Distance of pair: [DTE, TPR] is changed 1.2243 -> 0.9041\n",
      "Distance of pair: [DTE, VZ] is changed 1.0131 -> 0.8801\n",
      "Distance of pair: [DVN, EL] is changed 1.0763 -> 0.8903\n",
      "Distance of pair: [DVN, EMN] is changed 0.8254 -> 0.7669\n",
      "Distance of pair: [DVN, ESS] is changed 1.2237 -> 0.7893\n",
      "Distance of pair: [DVN, EXPD] is changed 1.0495 -> 0.8444\n",
      "Distance of pair: [DVN, FAST] is changed 0.9936 -> 0.7783\n",
      "Distance of pair: [DVN, FLIR] is changed 1.0452 -> 0.8712\n",
      "Distance of pair: [DVN, FMC] is changed 0.8931 -> 0.7669\n",
      "Distance of pair: [DVN, GLW] is changed 1.0116 -> 0.7669\n",
      "Distance of pair: [DVN, GPS] is changed 0.9914 -> 0.9041\n",
      "Distance of pair: [DVN, JBHT] is changed 1.0256 -> 0.8829\n",
      "Distance of pair: [DVN, LOW] is changed 0.9700 -> 0.8407\n",
      "Distance of pair: [DVN, MAR] is changed 1.0402 -> 0.8664\n",
      "Distance of pair: [DVN, MET] is changed 0.7725 -> 0.7669\n",
      "Distance of pair: [DVN, MU] is changed 0.9907 -> 0.8458\n",
      "Distance of pair: [DVN, NI] is changed 1.1686 -> 0.7893\n",
      "Distance of pair: [DVN, NUE] is changed 0.8390 -> 0.7669\n",
      "Distance of pair: [DVN, NWSA] is changed 1.0087 -> 0.8632\n",
      "Distance of pair: [DVN, PKI] is changed 1.0609 -> 0.8179\n",
      "Distance of pair: [DVN, PM] is changed 1.1755 -> 1.0487\n",
      "Distance of pair: [DVN, PRGO] is changed 1.0949 -> 1.0298\n",
      "Distance of pair: [DVN, PYPL] is changed 1.0843 -> 0.7974\n",
      "Distance of pair: [DVN, RE] is changed 1.0890 -> 0.7669\n",
      "Distance of pair: [DVN, SLG] is changed 0.9978 -> 0.7893\n",
      "Distance of pair: [DVN, SNA] is changed 1.0246 -> 0.7707\n",
      "Distance of pair: [DVN, SO] is changed 1.2038 -> 0.7893\n",
      "Distance of pair: [DVN, SPGI] is changed 1.0201 -> 0.7808\n",
      "Distance of pair: [DVN, SWKS] is changed 1.0123 -> 0.8458\n",
      "Distance of pair: [DVN, SYF] is changed 0.8933 -> 0.7669\n",
      "Distance of pair: [DVN, T] is changed 1.0990 -> 0.8801\n",
      "Distance of pair: [DVN, TPR] is changed 0.9841 -> 0.9041\n",
      "Distance of pair: [DVN, VZ] is changed 1.3047 -> 0.8801\n",
      "Distance of pair: [EL, EMN] is changed 1.0151 -> 0.8903\n",
      "Distance of pair: [EL, EOG] is changed 1.0757 -> 0.8903\n",
      "Distance of pair: [EL, ESS] is changed 1.0986 -> 0.8903\n",
      "Distance of pair: [EL, EXPD] is changed 1.0579 -> 0.8903\n",
      "Distance of pair: [EL, FAST] is changed 1.0115 -> 0.8903\n",
      "Distance of pair: [EL, FLIR] is changed 1.0072 -> 0.8903\n",
      "Distance of pair: [EL, FMC] is changed 0.9455 -> 0.8903\n",
      "Distance of pair: [EL, GLW] is changed 0.9867 -> 0.8903\n",
      "Distance of pair: [EL, GPS] is changed 1.1122 -> 0.9041\n",
      "Distance of pair: [EL, JBHT] is changed 1.0730 -> 0.8903\n",
      "Distance of pair: [EL, JPM] is changed 0.9991 -> 0.8903\n",
      "Distance of pair: [EL, LOW] is changed 1.0412 -> 0.8903\n",
      "Distance of pair: [EL, MAR] is changed 1.0020 -> 0.8903\n",
      "Distance of pair: [EL, MET] is changed 0.9444 -> 0.8903\n",
      "Distance of pair: [EL, MU] is changed 1.0867 -> 0.8903\n",
      "Distance of pair: [EL, NI] is changed 1.0869 -> 0.8903\n",
      "Distance of pair: [EL, NOV] is changed 1.1162 -> 0.8903\n",
      "Distance of pair: [EL, NUE] is changed 0.9998 -> 0.8903\n",
      "Distance of pair: [EL, NWSA] is changed 1.0065 -> 0.8903\n",
      "Distance of pair: [EL, PKI] is changed 1.0226 -> 0.8903\n",
      "Distance of pair: [EL, PM] is changed 1.1329 -> 1.0487\n",
      "Distance of pair: [EL, PRGO] is changed 1.2254 -> 1.0298\n",
      "Distance of pair: [EL, PYPL] is changed 0.9521 -> 0.8903\n",
      "Distance of pair: [EL, RE] is changed 1.0571 -> 0.8903\n",
      "Distance of pair: [EL, SLG] is changed 1.1202 -> 0.8903\n",
      "Distance of pair: [EL, SNA] is changed 1.0485 -> 0.8903\n",
      "Distance of pair: [EL, SO] is changed 1.1442 -> 0.8903\n",
      "Distance of pair: [EL, SPGI] is changed 0.9399 -> 0.8903\n",
      "Distance of pair: [EL, SWKS] is changed 1.0743 -> 0.8903\n",
      "Distance of pair: [EL, SYF] is changed 0.9759 -> 0.8903\n",
      "Distance of pair: [EL, T] is changed 1.0854 -> 0.8903\n",
      "Distance of pair: [EL, TPR] is changed 1.0522 -> 0.9041\n",
      "Distance of pair: [EL, VZ] is changed 1.1333 -> 0.8903\n",
      "Distance of pair: [EMN, EOG] is changed 0.8620 -> 0.7669\n",
      "Distance of pair: [EMN, ESS] is changed 1.2380 -> 0.7893\n",
      "Distance of pair: [EMN, EXPD] is changed 0.8980 -> 0.8444\n",
      "Distance of pair: [EMN, FAST] is changed 0.8090 -> 0.7783\n",
      "Distance of pair: [EMN, FLIR] is changed 0.9030 -> 0.8712\n",
      "Distance of pair: [EMN, FMC] is changed 0.8008 -> 0.7353\n",
      "Distance of pair: [EMN, GLW] is changed 0.7630 -> 0.7526\n",
      "Distance of pair: [EMN, GPS] is changed 0.9125 -> 0.9041\n",
      "Distance of pair: [EMN, JBHT] is changed 0.9116 -> 0.8829\n",
      "Distance of pair: [EMN, JPM] is changed 0.6961 -> 0.6868\n",
      "Distance of pair: [EMN, LOW] is changed 0.9538 -> 0.8407\n",
      "Distance of pair: [EMN, MET] is changed 0.7021 -> 0.6868\n",
      "Distance of pair: [EMN, MU] is changed 0.9572 -> 0.8458\n",
      "Distance of pair: [EMN, NI] is changed 1.2134 -> 0.7893\n",
      "Distance of pair: [EMN, NOV] is changed 0.8992 -> 0.7669\n",
      "Distance of pair: [EMN, NWSA] is changed 0.9117 -> 0.8632\n",
      "Distance of pair: [EMN, PKI] is changed 0.9099 -> 0.8179\n",
      "Distance of pair: [EMN, PM] is changed 1.1446 -> 1.0487\n",
      "Distance of pair: [EMN, PRGO] is changed 1.0527 -> 1.0298\n",
      "Distance of pair: [EMN, PYPL] is changed 1.0406 -> 0.7974\n",
      "Distance of pair: [EMN, RE] is changed 1.0563 -> 0.7587\n",
      "Distance of pair: [EMN, SLG] is changed 0.9957 -> 0.7893\n",
      "Distance of pair: [EMN, SNA] is changed 0.7931 -> 0.7707\n",
      "Distance of pair: [EMN, SO] is changed 1.2586 -> 0.7893\n",
      "Distance of pair: [EMN, SPGI] is changed 1.0301 -> 0.7808\n",
      "Distance of pair: [EMN, SYF] is changed 0.8104 -> 0.6868\n",
      "Distance of pair: [EMN, T] is changed 1.0143 -> 0.8801\n",
      "Distance of pair: [EMN, TPR] is changed 0.9359 -> 0.9041\n",
      "Distance of pair: [EMN, VZ] is changed 1.1984 -> 0.8801\n",
      "Distance of pair: [EOG, ESS] is changed 1.2205 -> 0.7893\n",
      "Distance of pair: [EOG, EXPD] is changed 1.0698 -> 0.8444\n",
      "Distance of pair: [EOG, FAST] is changed 1.0092 -> 0.7783\n",
      "Distance of pair: [EOG, FLIR] is changed 1.0691 -> 0.8712\n",
      "Distance of pair: [EOG, FMC] is changed 0.8994 -> 0.7669\n",
      "Distance of pair: [EOG, GLW] is changed 1.0152 -> 0.7669\n",
      "Distance of pair: [EOG, GPS] is changed 1.0142 -> 0.9041\n",
      "Distance of pair: [EOG, JBHT] is changed 1.0254 -> 0.8829\n",
      "Distance of pair: [EOG, JPM] is changed 0.7888 -> 0.7669\n",
      "Distance of pair: [EOG, LOW] is changed 0.9864 -> 0.8407\n",
      "Distance of pair: [EOG, MAR] is changed 1.0586 -> 0.8664\n",
      "Distance of pair: [EOG, MET] is changed 0.7906 -> 0.7669\n",
      "Distance of pair: [EOG, MU] is changed 1.0104 -> 0.8458\n",
      "Distance of pair: [EOG, NI] is changed 1.1666 -> 0.7893\n",
      "Distance of pair: [EOG, NOV] is changed 0.7104 -> 0.6623\n",
      "Distance of pair: [EOG, NUE] is changed 0.8347 -> 0.7669\n",
      "Distance of pair: [EOG, NWSA] is changed 1.0093 -> 0.8632\n",
      "Distance of pair: [EOG, PKI] is changed 1.0606 -> 0.8179\n",
      "Distance of pair: [EOG, PM] is changed 1.1640 -> 1.0487\n",
      "Distance of pair: [EOG, PRGO] is changed 1.1437 -> 1.0298\n",
      "Distance of pair: [EOG, PYPL] is changed 1.0965 -> 0.7974\n",
      "Distance of pair: [EOG, RE] is changed 1.1110 -> 0.7669\n",
      "Distance of pair: [EOG, SLG] is changed 1.0203 -> 0.7893\n",
      "Distance of pair: [EOG, SNA] is changed 1.0294 -> 0.7707\n",
      "Distance of pair: [EOG, SO] is changed 1.2104 -> 0.7893\n",
      "Distance of pair: [EOG, SPGI] is changed 1.0281 -> 0.7808\n",
      "Distance of pair: [EOG, SWKS] is changed 1.0377 -> 0.8458\n",
      "Distance of pair: [EOG, SYF] is changed 0.9175 -> 0.7669\n",
      "Distance of pair: [EOG, T] is changed 1.0899 -> 0.8801\n",
      "Distance of pair: [EOG, TPR] is changed 1.0029 -> 0.9041\n",
      "Distance of pair: [EOG, VZ] is changed 1.3019 -> 0.8801\n",
      "Distance of pair: [ESS, EXPD] is changed 1.2209 -> 0.8444\n",
      "Distance of pair: [ESS, FAST] is changed 1.1789 -> 0.7893\n",
      "Distance of pair: [ESS, FLIR] is changed 1.0168 -> 0.8712\n",
      "Distance of pair: [ESS, FMC] is changed 1.1210 -> 0.7893\n",
      "Distance of pair: [ESS, GLW] is changed 1.1850 -> 0.7893\n",
      "Distance of pair: [ESS, GPS] is changed 1.3041 -> 0.9041\n",
      "Distance of pair: [ESS, JBHT] is changed 1.1630 -> 0.8829\n",
      "Distance of pair: [ESS, JPM] is changed 1.0514 -> 0.7893\n",
      "Distance of pair: [ESS, LOW] is changed 1.0946 -> 0.8407\n",
      "Distance of pair: [ESS, MAR] is changed 1.2389 -> 0.8664\n",
      "Distance of pair: [ESS, MET] is changed 1.0525 -> 0.7893\n",
      "Distance of pair: [ESS, MU] is changed 1.2672 -> 0.8458\n",
      "Distance of pair: [ESS, NI] is changed 0.8946 -> 0.7624\n",
      "Distance of pair: [ESS, NOV] is changed 1.2340 -> 0.7893\n",
      "Distance of pair: [ESS, NUE] is changed 1.1681 -> 0.7893\n",
      "Distance of pair: [ESS, NWSA] is changed 1.1142 -> 0.8632\n",
      "Distance of pair: [ESS, PKI] is changed 1.1777 -> 0.8179\n",
      "Distance of pair: [ESS, PM] is changed 1.1089 -> 1.0487\n",
      "Distance of pair: [ESS, PRGO] is changed 1.2415 -> 1.0298\n",
      "Distance of pair: [ESS, PYPL] is changed 1.0851 -> 0.7974\n",
      "Distance of pair: [ESS, RE] is changed 0.9896 -> 0.7893\n",
      "Distance of pair: [ESS, SLG] is changed 0.8785 -> 0.7639\n",
      "Distance of pair: [ESS, SNA] is changed 1.1691 -> 0.7893\n",
      "Distance of pair: [ESS, SO] is changed 0.8721 -> 0.7624\n",
      "Distance of pair: [ESS, SPGI] is changed 0.9242 -> 0.7893\n",
      "Distance of pair: [ESS, SWKS] is changed 1.2119 -> 0.8458\n",
      "Distance of pair: [ESS, SYF] is changed 1.0353 -> 0.7893\n",
      "Distance of pair: [ESS, T] is changed 1.0576 -> 0.8801\n",
      "Distance of pair: [ESS, TPR] is changed 1.2610 -> 0.9041\n",
      "Distance of pair: [ESS, VZ] is changed 1.0869 -> 0.8801\n",
      "Distance of pair: [EXPD, FAST] is changed 0.8451 -> 0.8444\n",
      "Distance of pair: [EXPD, FLIR] is changed 0.9893 -> 0.8712\n",
      "Distance of pair: [EXPD, FMC] is changed 0.9400 -> 0.8444\n",
      "Distance of pair: [EXPD, GLW] is changed 0.9550 -> 0.8444\n",
      "Distance of pair: [EXPD, GPS] is changed 1.0210 -> 0.9041\n",
      "Distance of pair: [EXPD, JBHT] is changed 0.8907 -> 0.8829\n",
      "Distance of pair: [EXPD, JPM] is changed 0.9266 -> 0.8444\n",
      "Distance of pair: [EXPD, LOW] is changed 1.0559 -> 0.8444\n",
      "Distance of pair: [EXPD, MAR] is changed 0.9968 -> 0.8664\n",
      "Distance of pair: [EXPD, MET] is changed 0.9132 -> 0.8444\n",
      "Distance of pair: [EXPD, MU] is changed 1.0702 -> 0.8458\n",
      "Distance of pair: [EXPD, NI] is changed 1.2173 -> 0.8444\n",
      "Distance of pair: [EXPD, NOV] is changed 1.1141 -> 0.8444\n",
      "Distance of pair: [EXPD, NUE] is changed 0.9104 -> 0.8444\n",
      "Distance of pair: [EXPD, NWSA] is changed 0.9997 -> 0.8632\n",
      "Distance of pair: [EXPD, PKI] is changed 1.0057 -> 0.8444\n",
      "Distance of pair: [EXPD, PM] is changed 1.1664 -> 1.0487\n",
      "Distance of pair: [EXPD, PRGO] is changed 1.0856 -> 1.0298\n",
      "Distance of pair: [EXPD, PYPL] is changed 1.1207 -> 0.8444\n",
      "Distance of pair: [EXPD, RE] is changed 1.1156 -> 0.8444\n",
      "Distance of pair: [EXPD, SLG] is changed 1.1164 -> 0.8444\n",
      "Distance of pair: [EXPD, SNA] is changed 0.9356 -> 0.8444\n",
      "Distance of pair: [EXPD, SO] is changed 1.2721 -> 0.8444\n",
      "Distance of pair: [EXPD, SPGI] is changed 1.0702 -> 0.8444\n",
      "Distance of pair: [EXPD, SWKS] is changed 0.9807 -> 0.8458\n",
      "Distance of pair: [EXPD, SYF] is changed 0.9866 -> 0.8444\n",
      "Distance of pair: [EXPD, T] is changed 1.1119 -> 0.8801\n",
      "Distance of pair: [EXPD, TPR] is changed 1.0279 -> 0.9041\n",
      "Distance of pair: [EXPD, VZ] is changed 1.2155 -> 0.8801\n",
      "Distance of pair: [FAST, FLIR] is changed 0.9788 -> 0.8712\n",
      "Distance of pair: [FAST, FMC] is changed 0.8500 -> 0.7783\n",
      "Distance of pair: [FAST, GLW] is changed 0.8747 -> 0.7783\n",
      "Distance of pair: [FAST, GPS] is changed 0.9916 -> 0.9041\n",
      "Distance of pair: [FAST, JBHT] is changed 0.9195 -> 0.8829\n",
      "Distance of pair: [FAST, JPM] is changed 0.8218 -> 0.7783\n",
      "Distance of pair: [FAST, LOW] is changed 0.9662 -> 0.8407\n",
      "Distance of pair: [FAST, MAR] is changed 0.9304 -> 0.8664\n",
      "Distance of pair: [FAST, MET] is changed 0.8398 -> 0.7783\n",
      "Distance of pair: [FAST, MU] is changed 0.9974 -> 0.8458\n",
      "Distance of pair: [FAST, NI] is changed 1.1910 -> 0.7893\n",
      "Distance of pair: [FAST, NOV] is changed 1.0167 -> 0.7783\n",
      "Distance of pair: [FAST, NWSA] is changed 0.9263 -> 0.8632\n",
      "Distance of pair: [FAST, PKI] is changed 1.0146 -> 0.8179\n",
      "Distance of pair: [FAST, PM] is changed 1.1988 -> 1.0487\n",
      "Distance of pair: [FAST, PRGO] is changed 1.1183 -> 1.0298\n",
      "Distance of pair: [FAST, PYPL] is changed 1.0379 -> 0.7974\n",
      "Distance of pair: [FAST, RE] is changed 1.0750 -> 0.7783\n",
      "Distance of pair: [FAST, SLG] is changed 1.0392 -> 0.7893\n",
      "Distance of pair: [FAST, SNA] is changed 0.8006 -> 0.7783\n",
      "Distance of pair: [FAST, SO] is changed 1.2695 -> 0.7893\n",
      "Distance of pair: [FAST, SPGI] is changed 1.0618 -> 0.7808\n",
      "Distance of pair: [FAST, SWKS] is changed 0.9498 -> 0.8458\n",
      "Distance of pair: [FAST, SYF] is changed 0.8951 -> 0.7783\n",
      "Distance of pair: [FAST, T] is changed 1.0964 -> 0.8801\n",
      "Distance of pair: [FAST, TPR] is changed 1.0074 -> 0.9041\n",
      "Distance of pair: [FAST, VZ] is changed 1.2585 -> 0.8801\n",
      "Distance of pair: [FLIR, FMC] is changed 0.9717 -> 0.8712\n",
      "Distance of pair: [FLIR, GLW] is changed 0.9316 -> 0.8712\n",
      "Distance of pair: [FLIR, GPS] is changed 1.1148 -> 0.9041\n",
      "Distance of pair: [FLIR, JBHT] is changed 1.0145 -> 0.8829\n",
      "Distance of pair: [FLIR, JPM] is changed 0.8810 -> 0.8712\n",
      "Distance of pair: [FLIR, LOW] is changed 1.0513 -> 0.8712\n",
      "Distance of pair: [FLIR, MAR] is changed 1.0677 -> 0.8712\n",
      "Distance of pair: [FLIR, MET] is changed 0.8868 -> 0.8712\n",
      "Distance of pair: [FLIR, MU] is changed 1.0666 -> 0.8712\n",
      "Distance of pair: [FLIR, NI] is changed 1.2240 -> 0.8712\n",
      "Distance of pair: [FLIR, NOV] is changed 1.1012 -> 0.8712\n",
      "Distance of pair: [FLIR, NUE] is changed 0.9250 -> 0.8712\n",
      "Distance of pair: [FLIR, NWSA] is changed 1.0290 -> 0.8712\n",
      "Distance of pair: [FLIR, PKI] is changed 1.0430 -> 0.8712\n",
      "Distance of pair: [FLIR, PM] is changed 1.1547 -> 1.0487\n",
      "Distance of pair: [FLIR, PRGO] is changed 1.0335 -> 1.0298\n",
      "Distance of pair: [FLIR, PYPL] is changed 1.0461 -> 0.8712\n",
      "Distance of pair: [FLIR, RE] is changed 1.0346 -> 0.8712\n",
      "Distance of pair: [FLIR, SLG] is changed 1.0143 -> 0.8712\n",
      "Distance of pair: [FLIR, SNA] is changed 1.0019 -> 0.8712\n",
      "Distance of pair: [FLIR, SO] is changed 1.1505 -> 0.8712\n",
      "Distance of pair: [FLIR, SPGI] is changed 0.9791 -> 0.8712\n",
      "Distance of pair: [FLIR, SWKS] is changed 0.9547 -> 0.8712\n",
      "Distance of pair: [FLIR, SYF] is changed 0.9631 -> 0.8712\n",
      "Distance of pair: [FLIR, T] is changed 1.1301 -> 0.8801\n",
      "Distance of pair: [FLIR, TPR] is changed 1.1141 -> 0.9041\n",
      "Distance of pair: [FLIR, VZ] is changed 1.1914 -> 0.8801\n",
      "Distance of pair: [FMC, GLW] is changed 0.9027 -> 0.7526\n",
      "Distance of pair: [FMC, GPS] is changed 1.0137 -> 0.9041\n",
      "Distance of pair: [FMC, JBHT] is changed 0.9588 -> 0.8829\n",
      "Distance of pair: [FMC, JPM] is changed 0.7838 -> 0.7353\n",
      "Distance of pair: [FMC, LOW] is changed 0.9170 -> 0.8407\n",
      "Distance of pair: [FMC, MAR] is changed 0.9197 -> 0.8664\n",
      "Distance of pair: [FMC, MU] is changed 0.9701 -> 0.8458\n",
      "Distance of pair: [FMC, NI] is changed 1.0757 -> 0.7893\n",
      "Distance of pair: [FMC, NOV] is changed 0.9842 -> 0.7669\n",
      "Distance of pair: [FMC, NUE] is changed 0.7878 -> 0.7353\n",
      "Distance of pair: [FMC, NWSA] is changed 0.9359 -> 0.8632\n",
      "Distance of pair: [FMC, PKI] is changed 0.9709 -> 0.8179\n",
      "Distance of pair: [FMC, PM] is changed 1.1358 -> 1.0487\n",
      "Distance of pair: [FMC, PRGO] is changed 1.1309 -> 1.0298\n",
      "Distance of pair: [FMC, PYPL] is changed 0.9234 -> 0.7974\n",
      "Distance of pair: [FMC, RE] is changed 1.0067 -> 0.7587\n",
      "Distance of pair: [FMC, SLG] is changed 0.9880 -> 0.7893\n",
      "Distance of pair: [FMC, SNA] is changed 0.8922 -> 0.7707\n",
      "Distance of pair: [FMC, SO] is changed 1.1382 -> 0.7893\n",
      "Distance of pair: [FMC, SPGI] is changed 0.8883 -> 0.7808\n",
      "Distance of pair: [FMC, SWKS] is changed 0.9123 -> 0.8458\n",
      "Distance of pair: [FMC, SYF] is changed 0.8206 -> 0.7353\n",
      "Distance of pair: [FMC, T] is changed 1.0236 -> 0.8801\n",
      "Distance of pair: [FMC, TPR] is changed 0.9492 -> 0.9041\n",
      "Distance of pair: [FMC, VZ] is changed 1.2196 -> 0.8801\n",
      "Distance of pair: [GLW, GPS] is changed 0.9661 -> 0.9041\n",
      "Distance of pair: [GLW, JBHT] is changed 0.9996 -> 0.8829\n",
      "Distance of pair: [GLW, JPM] is changed 0.8543 -> 0.7526\n",
      "Distance of pair: [GLW, LOW] is changed 0.9810 -> 0.8407\n",
      "Distance of pair: [GLW, MAR] is changed 0.9421 -> 0.8664\n",
      "Distance of pair: [GLW, MET] is changed 0.8508 -> 0.7526\n",
      "Distance of pair: [GLW, MU] is changed 0.9815 -> 0.8458\n",
      "Distance of pair: [GLW, NI] is changed 1.2200 -> 0.7893\n",
      "Distance of pair: [GLW, NOV] is changed 1.0835 -> 0.7669\n",
      "Distance of pair: [GLW, NUE] is changed 0.8748 -> 0.7526\n",
      "Distance of pair: [GLW, NWSA] is changed 0.9534 -> 0.8632\n",
      "Distance of pair: [GLW, PM] is changed 1.1660 -> 1.0487\n",
      "Distance of pair: [GLW, PRGO] is changed 1.1012 -> 1.0298\n",
      "Distance of pair: [GLW, PYPL] is changed 1.0037 -> 0.7974\n",
      "Distance of pair: [GLW, RE] is changed 1.0988 -> 0.7587\n",
      "Distance of pair: [GLW, SLG] is changed 1.0715 -> 0.7893\n",
      "Distance of pair: [GLW, SNA] is changed 0.8828 -> 0.7707\n",
      "Distance of pair: [GLW, SO] is changed 1.2593 -> 0.7893\n",
      "Distance of pair: [GLW, SPGI] is changed 1.0299 -> 0.7808\n",
      "Distance of pair: [GLW, SWKS] is changed 0.8846 -> 0.8458\n",
      "Distance of pair: [GLW, SYF] is changed 0.8992 -> 0.7526\n",
      "Distance of pair: [GLW, T] is changed 1.0468 -> 0.8801\n",
      "Distance of pair: [GLW, TPR] is changed 0.9722 -> 0.9041\n",
      "Distance of pair: [GLW, VZ] is changed 1.2117 -> 0.8801\n",
      "Distance of pair: [GPS, JBHT] is changed 1.0133 -> 0.9041\n",
      "Distance of pair: [GPS, JPM] is changed 0.9329 -> 0.9041\n",
      "Distance of pair: [GPS, LOW] is changed 1.0054 -> 0.9041\n",
      "Distance of pair: [GPS, MAR] is changed 0.9981 -> 0.9041\n",
      "Distance of pair: [GPS, MET] is changed 0.9231 -> 0.9041\n",
      "Distance of pair: [GPS, MU] is changed 1.0637 -> 0.9041\n",
      "Distance of pair: [GPS, NI] is changed 1.3100 -> 0.9041\n",
      "Distance of pair: [GPS, NOV] is changed 1.0345 -> 0.9041\n",
      "Distance of pair: [GPS, NWSA] is changed 0.9903 -> 0.9041\n",
      "Distance of pair: [GPS, PKI] is changed 1.0886 -> 0.9041\n",
      "Distance of pair: [GPS, PM] is changed 1.2063 -> 1.0487\n",
      "Distance of pair: [GPS, PRGO] is changed 1.1374 -> 1.0298\n",
      "Distance of pair: [GPS, PYPL] is changed 1.1761 -> 0.9041\n",
      "Distance of pair: [GPS, RE] is changed 1.2051 -> 0.9041\n",
      "Distance of pair: [GPS, SLG] is changed 1.1111 -> 0.9041\n",
      "Distance of pair: [GPS, SNA] is changed 0.9718 -> 0.9041\n",
      "Distance of pair: [GPS, SO] is changed 1.3570 -> 0.9041\n",
      "Distance of pair: [GPS, SPGI] is changed 1.1917 -> 0.9041\n",
      "Distance of pair: [GPS, SWKS] is changed 1.0301 -> 0.9041\n",
      "Distance of pair: [GPS, SYF] is changed 0.9787 -> 0.9041\n",
      "Distance of pair: [GPS, T] is changed 1.1090 -> 0.9041\n",
      "Distance of pair: [GPS, VZ] is changed 1.2371 -> 0.9041\n",
      "Distance of pair: [JBHT, JPM] is changed 0.8988 -> 0.8829\n",
      "Distance of pair: [JBHT, LOW] is changed 0.9594 -> 0.8829\n",
      "Distance of pair: [JBHT, MAR] is changed 1.0289 -> 0.8829\n",
      "Distance of pair: [JBHT, MET] is changed 0.8946 -> 0.8829\n",
      "Distance of pair: [JBHT, MU] is changed 1.0531 -> 0.8829\n",
      "Distance of pair: [JBHT, NI] is changed 1.1761 -> 0.8829\n",
      "Distance of pair: [JBHT, NOV] is changed 1.0110 -> 0.8829\n",
      "Distance of pair: [JBHT, NUE] is changed 0.9069 -> 0.8829\n",
      "Distance of pair: [JBHT, NWSA] is changed 1.0307 -> 0.8829\n",
      "Distance of pair: [JBHT, PKI] is changed 1.0504 -> 0.8829\n",
      "Distance of pair: [JBHT, PM] is changed 1.1538 -> 1.0487\n",
      "Distance of pair: [JBHT, PRGO] is changed 1.1447 -> 1.0298\n",
      "Distance of pair: [JBHT, PYPL] is changed 1.1105 -> 0.8829\n",
      "Distance of pair: [JBHT, RE] is changed 1.1182 -> 0.8829\n",
      "Distance of pair: [JBHT, SLG] is changed 1.0839 -> 0.8829\n",
      "Distance of pair: [JBHT, SNA] is changed 0.9584 -> 0.8829\n",
      "Distance of pair: [JBHT, SO] is changed 1.2703 -> 0.8829\n",
      "Distance of pair: [JBHT, SPGI] is changed 1.0457 -> 0.8829\n",
      "Distance of pair: [JBHT, SWKS] is changed 1.0541 -> 0.8829\n",
      "Distance of pair: [JBHT, SYF] is changed 0.9677 -> 0.8829\n",
      "Distance of pair: [JBHT, T] is changed 1.1246 -> 0.8829\n",
      "Distance of pair: [JBHT, TPR] is changed 1.0026 -> 0.9041\n",
      "Distance of pair: [JBHT, VZ] is changed 1.2568 -> 0.8829\n",
      "Distance of pair: [JPM, LOW] is changed 0.8718 -> 0.8407\n",
      "Distance of pair: [JPM, MAR] is changed 0.8789 -> 0.8664\n",
      "Distance of pair: [JPM, MU] is changed 0.9716 -> 0.8458\n",
      "Distance of pair: [JPM, NI] is changed 1.1282 -> 0.7893\n",
      "Distance of pair: [JPM, NOV] is changed 0.8220 -> 0.7669\n",
      "Distance of pair: [JPM, NUE] is changed 0.6954 -> 0.6868\n",
      "Distance of pair: [JPM, NWSA] is changed 0.8773 -> 0.8632\n",
      "Distance of pair: [JPM, PKI] is changed 0.9722 -> 0.8179\n",
      "Distance of pair: [JPM, PM] is changed 1.0956 -> 1.0487\n",
      "Distance of pair: [JPM, PRGO] is changed 1.0426 -> 1.0298\n",
      "Distance of pair: [JPM, PYPL] is changed 0.9385 -> 0.7974\n",
      "Distance of pair: [JPM, RE] is changed 0.9207 -> 0.7587\n",
      "Distance of pair: [JPM, SLG] is changed 0.9393 -> 0.7893\n",
      "Distance of pair: [JPM, SNA] is changed 0.8318 -> 0.7707\n",
      "Distance of pair: [JPM, SO] is changed 1.1197 -> 0.7893\n",
      "Distance of pair: [JPM, SPGI] is changed 0.8633 -> 0.7808\n",
      "Distance of pair: [JPM, SWKS] is changed 0.9015 -> 0.8458\n",
      "Distance of pair: [JPM, T] is changed 0.9547 -> 0.8801\n",
      "Distance of pair: [JPM, TPR] is changed 0.9693 -> 0.9041\n",
      "Distance of pair: [JPM, VZ] is changed 1.1408 -> 0.8801\n",
      "Distance of pair: [LOW, MAR] is changed 1.0112 -> 0.8664\n",
      "Distance of pair: [LOW, MET] is changed 0.8505 -> 0.8407\n",
      "Distance of pair: [LOW, MU] is changed 1.0605 -> 0.8458\n",
      "Distance of pair: [LOW, NI] is changed 1.1191 -> 0.8407\n",
      "Distance of pair: [LOW, NOV] is changed 1.0161 -> 0.8407\n",
      "Distance of pair: [LOW, NUE] is changed 0.9009 -> 0.8407\n",
      "Distance of pair: [LOW, NWSA] is changed 1.0229 -> 0.8632\n",
      "Distance of pair: [LOW, PKI] is changed 1.0721 -> 0.8407\n",
      "Distance of pair: [LOW, PM] is changed 1.1631 -> 1.0487\n",
      "Distance of pair: [LOW, PRGO] is changed 1.1532 -> 1.0298\n",
      "Distance of pair: [LOW, PYPL] is changed 0.9965 -> 0.8407\n",
      "Distance of pair: [LOW, RE] is changed 1.0890 -> 0.8407\n",
      "Distance of pair: [LOW, SLG] is changed 1.0591 -> 0.8407\n",
      "Distance of pair: [LOW, SNA] is changed 0.9860 -> 0.8407\n",
      "Distance of pair: [LOW, SO] is changed 1.1592 -> 0.8407\n",
      "Distance of pair: [LOW, SPGI] is changed 0.9679 -> 0.8407\n",
      "Distance of pair: [LOW, SWKS] is changed 0.9710 -> 0.8458\n",
      "Distance of pair: [LOW, SYF] is changed 0.9460 -> 0.8407\n",
      "Distance of pair: [LOW, T] is changed 1.0498 -> 0.8801\n",
      "Distance of pair: [LOW, TPR] is changed 1.0400 -> 0.9041\n",
      "Distance of pair: [LOW, VZ] is changed 1.1671 -> 0.8801\n",
      "Distance of pair: [MAR, MET] is changed 0.8842 -> 0.8664\n",
      "Distance of pair: [MAR, MU] is changed 0.9558 -> 0.8664\n",
      "Distance of pair: [MAR, NI] is changed 1.2448 -> 0.8664\n",
      "Distance of pair: [MAR, NOV] is changed 1.0757 -> 0.8664\n",
      "Distance of pair: [MAR, NUE] is changed 0.8818 -> 0.8664\n",
      "Distance of pair: [MAR, NWSA] is changed 0.8879 -> 0.8664\n",
      "Distance of pair: [MAR, PKI] is changed 0.9717 -> 0.8664\n",
      "Distance of pair: [MAR, PM] is changed 1.2054 -> 1.0487\n",
      "Distance of pair: [MAR, PRGO] is changed 1.2254 -> 1.0298\n",
      "Distance of pair: [MAR, PYPL] is changed 0.9829 -> 0.8664\n",
      "Distance of pair: [MAR, RE] is changed 1.1469 -> 0.8664\n",
      "Distance of pair: [MAR, SLG] is changed 1.1088 -> 0.8664\n",
      "Distance of pair: [MAR, SNA] is changed 0.9013 -> 0.8664\n",
      "Distance of pair: [MAR, SO] is changed 1.3487 -> 0.8664\n",
      "Distance of pair: [MAR, SPGI] is changed 1.0180 -> 0.8664\n",
      "Distance of pair: [MAR, SWKS] is changed 0.8902 -> 0.8664\n",
      "Distance of pair: [MAR, SYF] is changed 0.8890 -> 0.8664\n",
      "Distance of pair: [MAR, T] is changed 1.0930 -> 0.8801\n",
      "Distance of pair: [MAR, TPR] is changed 0.9803 -> 0.9041\n",
      "Distance of pair: [MAR, VZ] is changed 1.2671 -> 0.8801\n",
      "Distance of pair: [MET, MU] is changed 0.9688 -> 0.8458\n",
      "Distance of pair: [MET, NI] is changed 1.0801 -> 0.7893\n",
      "Distance of pair: [MET, NOV] is changed 0.8255 -> 0.7669\n",
      "Distance of pair: [MET, PKI] is changed 0.9311 -> 0.8179\n",
      "Distance of pair: [MET, PM] is changed 1.0790 -> 1.0487\n",
      "Distance of pair: [MET, PRGO] is changed 1.0567 -> 1.0298\n",
      "Distance of pair: [MET, PYPL] is changed 0.9505 -> 0.7974\n",
      "Distance of pair: [MET, RE] is changed 0.8151 -> 0.7587\n",
      "Distance of pair: [MET, SLG] is changed 0.9453 -> 0.7893\n",
      "Distance of pair: [MET, SNA] is changed 0.8112 -> 0.7707\n",
      "Distance of pair: [MET, SO] is changed 1.0684 -> 0.7893\n",
      "Distance of pair: [MET, SPGI] is changed 0.8214 -> 0.7808\n",
      "Distance of pair: [MET, SWKS] is changed 0.8645 -> 0.8458\n",
      "Distance of pair: [MET, SYF] is changed 0.6485 -> 0.5937\n",
      "Distance of pair: [MET, TPR] is changed 0.9191 -> 0.9041\n",
      "Distance of pair: [MET, VZ] is changed 1.0953 -> 0.8801\n",
      "Distance of pair: [MU, NI] is changed 1.2259 -> 0.8458\n",
      "Distance of pair: [MU, NOV] is changed 1.0600 -> 0.8458\n",
      "Distance of pair: [MU, NUE] is changed 0.9419 -> 0.8458\n",
      "Distance of pair: [MU, NWSA] is changed 1.0097 -> 0.8632\n",
      "Distance of pair: [MU, PKI] is changed 1.0029 -> 0.8458\n",
      "Distance of pair: [MU, PM] is changed 1.1796 -> 1.0487\n",
      "Distance of pair: [MU, PRGO] is changed 1.1627 -> 1.0298\n",
      "Distance of pair: [MU, PYPL] is changed 0.9877 -> 0.8458\n",
      "Distance of pair: [MU, RE] is changed 1.2184 -> 0.8458\n",
      "Distance of pair: [MU, SLG] is changed 1.1668 -> 0.8458\n",
      "Distance of pair: [MU, SNA] is changed 1.0340 -> 0.8458\n",
      "Distance of pair: [MU, SO] is changed 1.3294 -> 0.8458\n",
      "Distance of pair: [MU, SPGI] is changed 1.0002 -> 0.8458\n",
      "Distance of pair: [MU, SYF] is changed 0.9829 -> 0.8458\n",
      "Distance of pair: [MU, T] is changed 1.1157 -> 0.8801\n",
      "Distance of pair: [MU, TPR] is changed 1.0868 -> 0.9041\n",
      "Distance of pair: [MU, VZ] is changed 1.2918 -> 0.8801\n",
      "Distance of pair: [NI, NOV] is changed 1.2191 -> 0.7893\n",
      "Distance of pair: [NI, NUE] is changed 1.1653 -> 0.7893\n",
      "Distance of pair: [NI, NWSA] is changed 1.1425 -> 0.8632\n",
      "Distance of pair: [NI, PKI] is changed 1.1461 -> 0.8179\n",
      "Distance of pair: [NI, PM] is changed 1.1025 -> 1.0487\n",
      "Distance of pair: [NI, PRGO] is changed 1.2709 -> 1.0298\n",
      "Distance of pair: [NI, PYPL] is changed 1.1013 -> 0.7974\n",
      "Distance of pair: [NI, RE] is changed 0.9921 -> 0.7893\n",
      "Distance of pair: [NI, SLG] is changed 0.9333 -> 0.7639\n",
      "Distance of pair: [NI, SNA] is changed 1.2051 -> 0.7893\n",
      "Distance of pair: [NI, SO] is changed 0.7436 -> 0.7125\n",
      "Distance of pair: [NI, SPGI] is changed 0.9644 -> 0.7893\n",
      "Distance of pair: [NI, SWKS] is changed 1.2356 -> 0.8458\n",
      "Distance of pair: [NI, SYF] is changed 1.1055 -> 0.7893\n",
      "Distance of pair: [NI, T] is changed 1.1018 -> 0.8801\n",
      "Distance of pair: [NI, TPR] is changed 1.2588 -> 0.9041\n",
      "Distance of pair: [NI, VZ] is changed 1.0831 -> 0.8801\n",
      "Distance of pair: [NOV, NUE] is changed 0.8935 -> 0.7669\n",
      "Distance of pair: [NOV, NWSA] is changed 1.0185 -> 0.8632\n",
      "Distance of pair: [NOV, PKI] is changed 1.1628 -> 0.8179\n",
      "Distance of pair: [NOV, PM] is changed 1.1945 -> 1.0487\n",
      "Distance of pair: [NOV, PRGO] is changed 1.1034 -> 1.0298\n",
      "Distance of pair: [NOV, PYPL] is changed 1.1647 -> 0.7974\n",
      "Distance of pair: [NOV, RE] is changed 1.1153 -> 0.7669\n",
      "Distance of pair: [NOV, SLG] is changed 1.0449 -> 0.7893\n",
      "Distance of pair: [NOV, SNA] is changed 1.0349 -> 0.7707\n",
      "Distance of pair: [NOV, SO] is changed 1.2712 -> 0.7893\n",
      "Distance of pair: [NOV, SPGI] is changed 1.0440 -> 0.7808\n",
      "Distance of pair: [NOV, SWKS] is changed 1.0889 -> 0.8458\n",
      "Distance of pair: [NOV, SYF] is changed 0.9164 -> 0.7669\n",
      "Distance of pair: [NOV, T] is changed 1.0914 -> 0.8801\n",
      "Distance of pair: [NOV, TPR] is changed 1.0231 -> 0.9041\n",
      "Distance of pair: [NOV, VZ] is changed 1.3155 -> 0.8801\n",
      "Distance of pair: [NUE, NWSA] is changed 0.8866 -> 0.8632\n",
      "Distance of pair: [NUE, PKI] is changed 0.9804 -> 0.8179\n",
      "Distance of pair: [NUE, PM] is changed 1.1059 -> 1.0487\n",
      "Distance of pair: [NUE, PYPL] is changed 1.0385 -> 0.7974\n",
      "Distance of pair: [NUE, RE] is changed 1.0261 -> 0.7587\n",
      "Distance of pair: [NUE, SLG] is changed 1.0070 -> 0.7893\n",
      "Distance of pair: [NUE, SNA] is changed 0.7750 -> 0.7707\n",
      "Distance of pair: [NUE, SO] is changed 1.2273 -> 0.7893\n",
      "Distance of pair: [NUE, SPGI] is changed 0.9866 -> 0.7808\n",
      "Distance of pair: [NUE, SWKS] is changed 0.8574 -> 0.8458\n",
      "Distance of pair: [NUE, SYF] is changed 0.8041 -> 0.6868\n",
      "Distance of pair: [NUE, T] is changed 0.9516 -> 0.8801\n",
      "Distance of pair: [NUE, TPR] is changed 0.9111 -> 0.9041\n",
      "Distance of pair: [NUE, VZ] is changed 1.1466 -> 0.8801\n",
      "Distance of pair: [NWSA, PKI] is changed 0.9995 -> 0.8632\n",
      "Distance of pair: [NWSA, PM] is changed 1.1593 -> 1.0487\n",
      "Distance of pair: [NWSA, PRGO] is changed 1.1824 -> 1.0298\n",
      "Distance of pair: [NWSA, PYPL] is changed 1.0698 -> 0.8632\n",
      "Distance of pair: [NWSA, RE] is changed 1.0944 -> 0.8632\n",
      "Distance of pair: [NWSA, SLG] is changed 1.1278 -> 0.8632\n",
      "Distance of pair: [NWSA, SNA] is changed 0.9725 -> 0.8632\n",
      "Distance of pair: [NWSA, SO] is changed 1.2460 -> 0.8632\n",
      "Distance of pair: [NWSA, SPGI] is changed 0.9968 -> 0.8632\n",
      "Distance of pair: [NWSA, SWKS] is changed 0.9665 -> 0.8632\n",
      "Distance of pair: [NWSA, SYF] is changed 0.9003 -> 0.8632\n",
      "Distance of pair: [NWSA, T] is changed 1.0641 -> 0.8801\n",
      "Distance of pair: [NWSA, TPR] is changed 1.0901 -> 0.9041\n",
      "Distance of pair: [NWSA, VZ] is changed 1.2178 -> 0.8801\n",
      "Distance of pair: [PKI, PM] is changed 1.1788 -> 1.0487\n",
      "Distance of pair: [PKI, PRGO] is changed 1.1632 -> 1.0298\n",
      "Distance of pair: [PKI, PYPL] is changed 0.9989 -> 0.8179\n",
      "Distance of pair: [PKI, RE] is changed 1.1081 -> 0.8179\n",
      "Distance of pair: [PKI, SLG] is changed 1.1096 -> 0.8179\n",
      "Distance of pair: [PKI, SNA] is changed 0.9767 -> 0.8179\n",
      "Distance of pair: [PKI, SO] is changed 1.2248 -> 0.8179\n",
      "Distance of pair: [PKI, SPGI] is changed 0.9655 -> 0.8179\n",
      "Distance of pair: [PKI, SWKS] is changed 0.9338 -> 0.8458\n",
      "Distance of pair: [PKI, SYF] is changed 0.9739 -> 0.8179\n",
      "Distance of pair: [PKI, T] is changed 1.1254 -> 0.8801\n",
      "Distance of pair: [PKI, TPR] is changed 1.0637 -> 0.9041\n",
      "Distance of pair: [PKI, VZ] is changed 1.2405 -> 0.8801\n",
      "Distance of pair: [PM, PRGO] is changed 1.1492 -> 1.0487\n",
      "Distance of pair: [PM, PYPL] is changed 1.2001 -> 1.0487\n",
      "Distance of pair: [PM, RE] is changed 1.1237 -> 1.0487\n",
      "Distance of pair: [PM, SLG] is changed 1.0931 -> 1.0487\n",
      "Distance of pair: [PM, SNA] is changed 1.2196 -> 1.0487\n",
      "Distance of pair: [PM, SO] is changed 1.1245 -> 1.0487\n",
      "Distance of pair: [PM, SPGI] is changed 1.1380 -> 1.0487\n",
      "Distance of pair: [PM, SWKS] is changed 1.1573 -> 1.0487\n",
      "Distance of pair: [PM, SYF] is changed 1.0543 -> 1.0487\n",
      "Distance of pair: [PM, TPR] is changed 1.2414 -> 1.0487\n",
      "Distance of pair: [PM, VZ] is changed 1.1381 -> 1.0487\n",
      "Distance of pair: [PRGO, PYPL] is changed 1.2229 -> 1.0298\n",
      "Distance of pair: [PRGO, RE] is changed 1.2045 -> 1.0298\n",
      "Distance of pair: [PRGO, SLG] is changed 1.1729 -> 1.0298\n",
      "Distance of pair: [PRGO, SNA] is changed 1.1388 -> 1.0298\n",
      "Distance of pair: [PRGO, SO] is changed 1.2227 -> 1.0298\n",
      "Distance of pair: [PRGO, SPGI] is changed 1.1887 -> 1.0298\n",
      "Distance of pair: [PRGO, SWKS] is changed 1.1048 -> 1.0298\n",
      "Distance of pair: [PRGO, SYF] is changed 1.1043 -> 1.0298\n",
      "Distance of pair: [PRGO, T] is changed 1.1236 -> 1.0298\n",
      "Distance of pair: [PRGO, TPR] is changed 1.0920 -> 1.0298\n",
      "Distance of pair: [PRGO, VZ] is changed 1.2031 -> 1.0298\n",
      "Distance of pair: [PYPL, RE] is changed 1.1162 -> 0.7974\n",
      "Distance of pair: [PYPL, SLG] is changed 1.0748 -> 0.7974\n",
      "Distance of pair: [PYPL, SNA] is changed 1.0457 -> 0.7974\n",
      "Distance of pair: [PYPL, SO] is changed 1.1865 -> 0.7974\n",
      "Distance of pair: [PYPL, SWKS] is changed 0.9851 -> 0.8458\n",
      "Distance of pair: [PYPL, SYF] is changed 0.9394 -> 0.7974\n",
      "Distance of pair: [PYPL, T] is changed 1.1869 -> 0.8801\n",
      "Distance of pair: [PYPL, TPR] is changed 1.1332 -> 0.9041\n",
      "Distance of pair: [PYPL, VZ] is changed 1.2375 -> 0.8801\n",
      "Distance of pair: [RE, SLG] is changed 1.0338 -> 0.7893\n",
      "Distance of pair: [RE, SNA] is changed 1.0352 -> 0.7707\n",
      "Distance of pair: [RE, SO] is changed 0.9454 -> 0.7893\n",
      "Distance of pair: [RE, SPGI] is changed 0.9700 -> 0.7808\n",
      "Distance of pair: [RE, SWKS] is changed 1.1086 -> 0.8458\n",
      "Distance of pair: [RE, SYF] is changed 0.9217 -> 0.7587\n",
      "Distance of pair: [RE, T] is changed 1.0201 -> 0.8801\n",
      "Distance of pair: [RE, TPR] is changed 1.1709 -> 0.9041\n",
      "Distance of pair: [RE, VZ] is changed 1.0809 -> 0.8801\n",
      "Distance of pair: [SLG, SNA] is changed 1.0577 -> 0.7893\n",
      "Distance of pair: [SLG, SO] is changed 0.9877 -> 0.7639\n",
      "Distance of pair: [SLG, SPGI] is changed 1.0219 -> 0.7893\n",
      "Distance of pair: [SLG, SWKS] is changed 1.1214 -> 0.8458\n",
      "Distance of pair: [SLG, SYF] is changed 0.9629 -> 0.7893\n",
      "Distance of pair: [SLG, T] is changed 1.0132 -> 0.8801\n",
      "Distance of pair: [SLG, TPR] is changed 1.1192 -> 0.9041\n",
      "Distance of pair: [SLG, VZ] is changed 1.1187 -> 0.8801\n",
      "Distance of pair: [SNA, SO] is changed 1.2698 -> 0.7893\n",
      "Distance of pair: [SNA, SPGI] is changed 1.0266 -> 0.7808\n",
      "Distance of pair: [SNA, SWKS] is changed 0.9163 -> 0.8458\n",
      "Distance of pair: [SNA, SYF] is changed 0.8813 -> 0.7707\n",
      "Distance of pair: [SNA, T] is changed 1.0509 -> 0.8801\n",
      "Distance of pair: [SNA, TPR] is changed 0.9632 -> 0.9041\n",
      "Distance of pair: [SNA, VZ] is changed 1.2192 -> 0.8801\n",
      "Distance of pair: [SO, SPGI] is changed 1.0282 -> 0.7893\n",
      "Distance of pair: [SO, SWKS] is changed 1.3075 -> 0.8458\n",
      "Distance of pair: [SO, SYF] is changed 1.1346 -> 0.7893\n",
      "Distance of pair: [SO, T] is changed 1.0682 -> 0.8801\n",
      "Distance of pair: [SO, TPR] is changed 1.3125 -> 0.9041\n",
      "Distance of pair: [SO, VZ] is changed 1.0261 -> 0.8801\n",
      "Distance of pair: [SPGI, SWKS] is changed 0.9739 -> 0.8458\n",
      "Distance of pair: [SPGI, SYF] is changed 0.8929 -> 0.7808\n",
      "Distance of pair: [SPGI, T] is changed 1.0338 -> 0.8801\n",
      "Distance of pair: [SPGI, TPR] is changed 1.1490 -> 0.9041\n",
      "Distance of pair: [SPGI, VZ] is changed 1.0991 -> 0.8801\n",
      "Distance of pair: [SWKS, SYF] is changed 0.8945 -> 0.8458\n",
      "Distance of pair: [SWKS, T] is changed 1.0700 -> 0.8801\n",
      "Distance of pair: [SWKS, TPR] is changed 1.0311 -> 0.9041\n",
      "Distance of pair: [SWKS, VZ] is changed 1.2459 -> 0.8801\n",
      "Distance of pair: [SYF, T] is changed 0.9363 -> 0.8801\n",
      "Distance of pair: [SYF, TPR] is changed 0.9808 -> 0.9041\n",
      "Distance of pair: [SYF, VZ] is changed 1.0998 -> 0.8801\n",
      "Distance of pair: [T, TPR] is changed 1.1536 -> 0.9041\n",
      "Distance of pair: [TPR, VZ] is changed 1.2983 -> 0.9041\n"
     ]
    }
   ],
   "source": [
    "# update distance\n",
    "num_changed = 0\n",
    "diff_store = []\n",
    "for stock_a, stock_b in itertools.combinations(distance_matrix.index, 2):\n",
    "    original_distance = distance_matrix.loc[stock_a, stock_b]\n",
    "    new_distance = get_filtered_distance_in_mst(mst, stock_a, stock_b)\n",
    "    if original_distance != new_distance:\n",
    "        num_changed +=1\n",
    "        filtered_ds_matrix.loc[stock_a, stock_b] = new_distance\n",
    "        print(f\"Distance of pair: [{stock_a}, {stock_b}] is changed {original_distance:.4f} -> {new_distance:.4f}\")\n",
    "        diff = original_distance - new_distance\n",
    "        diff_store.append((diff, stock_a, stock_b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see the distaces are chainging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 96.00% distances are changed\n"
     ]
    }
   ],
   "source": [
    "total_pair = len(list(itertools.combinations(distance_matrix.index, 2)))\n",
    "print(f\"total {num_changed*100/total_pair:2.2f}% distances are changed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, we can check that upper triangular part of matrix is changed below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADM</th>\n",
       "      <th>AGN</th>\n",
       "      <th>AIV</th>\n",
       "      <th>ALL</th>\n",
       "      <th>ALLE</th>\n",
       "      <th>AME</th>\n",
       "      <th>ARE</th>\n",
       "      <th>CHRW</th>\n",
       "      <th>CMI</th>\n",
       "      <th>COST</th>\n",
       "      <th>...</th>\n",
       "      <th>RE</th>\n",
       "      <th>SLG</th>\n",
       "      <th>SNA</th>\n",
       "      <th>SO</th>\n",
       "      <th>SPGI</th>\n",
       "      <th>SWKS</th>\n",
       "      <th>SYF</th>\n",
       "      <th>T</th>\n",
       "      <th>TPR</th>\n",
       "      <th>VZ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADM</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>0.939029</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>0.911257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>0.845789</td>\n",
       "      <td>0.827996</td>\n",
       "      <td>0.880133</td>\n",
       "      <td>0.904135</td>\n",
       "      <td>0.880133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGN</th>\n",
       "      <td>1.257855</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>...</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "      <td>1.170525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIV</th>\n",
       "      <td>1.090633</td>\n",
       "      <td>1.312443</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.789252</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.789252</td>\n",
       "      <td>0.642848</td>\n",
       "      <td>0.939029</td>\n",
       "      <td>0.789252</td>\n",
       "      <td>0.911257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.789252</td>\n",
       "      <td>0.763908</td>\n",
       "      <td>0.789252</td>\n",
       "      <td>0.762399</td>\n",
       "      <td>0.789252</td>\n",
       "      <td>0.845789</td>\n",
       "      <td>0.789252</td>\n",
       "      <td>0.880133</td>\n",
       "      <td>0.904135</td>\n",
       "      <td>0.880133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALL</th>\n",
       "      <td>0.881133</td>\n",
       "      <td>1.256959</td>\n",
       "      <td>0.940749</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.644946</td>\n",
       "      <td>0.789252</td>\n",
       "      <td>0.939029</td>\n",
       "      <td>0.716076</td>\n",
       "      <td>0.911257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.758673</td>\n",
       "      <td>0.789252</td>\n",
       "      <td>0.770697</td>\n",
       "      <td>0.789252</td>\n",
       "      <td>0.780800</td>\n",
       "      <td>0.845789</td>\n",
       "      <td>0.644946</td>\n",
       "      <td>0.880133</td>\n",
       "      <td>0.904135</td>\n",
       "      <td>0.880133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALLE</th>\n",
       "      <td>1.065659</td>\n",
       "      <td>1.248871</td>\n",
       "      <td>1.051387</td>\n",
       "      <td>0.968112</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.939029</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.911257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.892054</td>\n",
       "      <td>0.904135</td>\n",
       "      <td>0.892054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ADM       AGN       AIV       ALL      ALLE       AME       ARE  \\\n",
       "ADM   0.000000  1.170525  0.827996  0.827996  0.892054  0.827996  0.827996   \n",
       "AGN   1.257855  0.000000  1.170525  1.170525  1.170525  1.170525  1.170525   \n",
       "AIV   1.090633  1.312443  0.000000  0.789252  0.892054  0.789252  0.642848   \n",
       "ALL   0.881133  1.256959  0.940749  0.000000  0.892054  0.644946  0.789252   \n",
       "ALLE  1.065659  1.248871  1.051387  0.968112  0.000000  0.892054  0.892054   \n",
       "\n",
       "          CHRW       CMI      COST  ...        RE       SLG       SNA  \\\n",
       "ADM   0.939029  0.827996  0.911257  ...  0.827996  0.827996  0.827996   \n",
       "AGN   1.170525  1.170525  1.170525  ...  1.170525  1.170525  1.170525   \n",
       "AIV   0.939029  0.789252  0.911257  ...  0.789252  0.763908  0.789252   \n",
       "ALL   0.939029  0.716076  0.911257  ...  0.758673  0.789252  0.770697   \n",
       "ALLE  0.939029  0.892054  0.911257  ...  0.892054  0.892054  0.892054   \n",
       "\n",
       "            SO      SPGI      SWKS       SYF         T       TPR        VZ  \n",
       "ADM   0.827996  0.827996  0.845789  0.827996  0.880133  0.904135  0.880133  \n",
       "AGN   1.170525  1.170525  1.170525  1.170525  1.170525  1.170525  1.170525  \n",
       "AIV   0.762399  0.789252  0.845789  0.789252  0.880133  0.904135  0.880133  \n",
       "ALL   0.789252  0.780800  0.845789  0.644946  0.880133  0.904135  0.880133  \n",
       "ALLE  0.892054  0.892054  0.892054  0.892054  0.892054  0.904135  0.892054  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_ds_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering based on filtered distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is there any change in dendrograms due to the filtering?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's hard to find to difference of hierarchical clustering  with 'single' method from new distances. So, I chosse 'complete' method to show the effect of de-noise tric. because our de-noise trick basically changes rather long distance to existing short distance that already we know."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "hc_complete = linkage(squareform(distance_matrix), method=\"complete\")\n",
    "ddata = dendrogram(hc_complete, labels=distance_matrix.index)\n",
    "plt.xlabel('Tickers', fontsize=12)\n",
    "plt.ylabel('Distances', fontsize=12)\n",
    "plt.title('Hierarchical Clustering Dendrogram', fontsize=12)\n",
    "\n",
    "plt.subplot(122)\n",
    "new_hc_complete = linkage(get_condensed_distance_matrix(filtered_ds_matrix.to_numpy()), method=\"complete\")\n",
    "ddata = dendrogram(new_hc_complete, labels=filtered_ds_matrix.index)\n",
    "plt.xlabel('Tickers', fontsize=12)\n",
    "plt.ylabel('Distances', fontsize=12)\n",
    "plt.title('Hierarchical Clustering Dendrogram(de-noised)', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AYAuaWTBMcnqSlyZ5zZ6G7n7intdV9ftJvryX+R/e3Z9ft+oAAABGYmbBsLvfVVXbluurqkry40kesZE1AQAAjNFmPcfwIUmu7O5Pr9DfSd5eVe+vqh17W1BV7aiqnVW1c/fu3WteKAAAwLzbrMHw+CRn7qX/h7r7vkmOTvL0qnroSgO7+9Tu3t7d2xcWFta6TgAAgLm36YJhVe2b5EeTnLXSmO6+fHi+Kskbkxy+MdUBAABsPZsuGCZ5ZJJPdPdly3VW1a2q6tZ7Xid5dJKLNrA+AACALWVmwbCqzkzy7iR3r6rLquopQ9dPZMlhpFV1h6o6Z5g8MMkFVfWhJO9NcnZ3v22j6gYAANhqZnlV0uNXaD9xmbbPJjlmeH1Jknuva3EAAAAjshkPJQUAAGADCYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACMnGAIAAIycYAgAADBygiEAAMDICYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACMnGAIAAIycYAgAADBygiEAAMDICYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyAmGAAAAIycYAsAcqqpbVtV7q+pDVfXRqnrh0H56VX2mqi4cHofNulYANr99Z10AAHCjfCPJI7r7q1W1X5ILquqtQ9+vdvfrZ1gbAHNGMASAOdTdneSrw+R+w6NnVxEA88yhpAAwp6pqn6q6MMlVSc7t7vcMXf+tqj5cVadU1S1WmHdHVe2sqp27d+/esJoB2JwEQwCYU919XXcfluROSQ6vqnsleW6SeyS5X5LbJPm1FeY9tbu3d/f2hYWFDasZgM1JMASAOdfdX0ryziRHdfcVPfGNJK9OcvhsqwNgHgiGADCHqmqhqvYfXn9bkkcl+URVHTS0VZLHJblodlUCMC9cfAYA5tNBSc6oqn0y+aL3dd3911X1jqpaSFJJLkzyc7MsEoD5IBgCwBzq7g8nuc8y7Y+YQTkAzDmHkgIAAIycYAgAADBygiEAAMDICYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACMnGAIAAIycYAgAADBygiEAAMDICYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACM3s2BYVadV1VVVddGitpOq6vKqunB4HLPCvEdV1Ser6uKqes7GVQ0AALD1zHKP4elJjlqm/ZTuPmx4nLO0s6r2SfInSY5Ocs8kx1fVPde1UgAAgC1sZsGwu9+V5Is3YtbDk1zc3Zd0978n+Yskx61pcQAAACOyGc8xfEZVfXg41PSAZfrvmOTSRdOXDW3LqqodVbWzqnbu3r17rWsFAACYe5stGL48yV2THJbkiiS/f1MX2N2ndvf27t6+sLBwUxcHAACw5WyqYNjdV3b3dd39zSR/lslho0tdnuTgRdN3GtoAAAC4ETZVMKyqgxZNPj7JRcsMe1+Su1XVoVV18yQ/keQtG1EfAADAVrTvrFZcVWcmOSLJ7arqsiQvSHJEVR2WpJPsSvK0Yewdkryyu4/p7mur6hlJ/ibJPklO6+6PzuBHAAAA2BJmFgy7+/hlml+1wtjPJjlm0fQ5Sa53KwsAAACmt6kOJQUAAGDjCYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACMnGAIAAIycYAgAADBygiEAAMDICYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACMnGAIAAIycYAgAADBygiEAAMDICYYAMIeq6pZV9d6q+lBVfbSqXji0H1pV76mqi6vqrKq6+axrBWDzEwwBYD59I8kjuvveSQ5LclRVPSDJi5Oc0t3fneTqJE+ZYY0AzAnBEADmUE98dZjcb3h0kkckef3QfkaSx82gPADmjGAIAHOqqvapqguTXJXk3CT/lORL3X3tMOSyJHdcYd4dVbWzqnbu3r17YwoGYNMSDAFgTnX3dd19WJI7JTk8yT2mmPfU7t7e3dsXFhbWrUYA5oNgCABzrru/lOSdSR6YZP+q2nfoulOSy2dWGABzQzAEgDlUVQtVtf/w+tuSPCrJxzMJiE8Yhp2Q5M2zqRCAebLvDQ8BADahg5KcUVX7ZPJF7+u6+6+r6mNJ/qKqfifJB5O8apZFAjAfBEMAmEPd/eEk91mm/ZJMzjcEgFVzKCkAAMDICYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACMnGAIAAIycYAgAADBygiEAAMDICYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACMnGAIAAIycYAgAADBygiEAAMDICYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyM0sGFbVaVV1VVVdtKjtf1TVJ6rqw1X1xqraf4V5d1XVR6rqwqrauXFVAwAAbD2z3GN4epKjlrSdm+Re3f0DST6V5Ll7mf/h3X1Yd29fp/oAAABGYWbBsLvfleSLS9re3t3XDpP/mOROG14YAADAyGzmcwx/JslbV+jrJG+vqvdX1Y69LaSqdlTVzqrauXv37jUvEgAAYN5tymBYVb+R5Nokr11hyA91932THJ3k6VX10JWW1d2ndvf27t6+sLCwDtUCAADMt00XDKvqxCTHJvnJ7u7lxnT35cPzVUnemOTwDSsQAABgi9lUwbCqjkry7CQ/0t3/usKYW1XVrfe8TvLoJBctNxYAAIAbNsvbVZyZ5N1J7l5Vl1XVU5K8NMmtk5w73IriFcPYO1TVOcOsBya5oKo+lOS9Sc7u7rfN4EcAAADYEvad1Yq7+/hlml+1wtjPJjlmeH1JknuvY2kAAACjsqkOJQUAAGDjCYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACMnGAIAAIycYAgAADByaxIMq+oWa7EcAAAANt6qg2FVHV1VJy1p+4WquibJ16rqz6tqv7UuEAAAgPU1zR7DX01yjz0TVfW9SV6S5LNJzk3yxCRPX9PqAAAAWHfTBMPvTbJz0fQTk3w9yeHdfXSSs5KcsIa1AQAAsAGmCYYHJPn8oulHJnlHd18zTJ+f5NA1qgsAAIANMk0w/HySOydJVd06yf2S/N2i/v2S7LN2pQEAALAR9p1i7LuT/FxVfTTJ0cO8b13U/91JrljD2gAAANgA0+wxfMEw/nVJfjrJa7r7Y0lSVZXk8Un+fs0rBACup6oOrqp3VtXHquqjVfXMof2kqrq8qi4cHsfMulYANr9V7zHs7o8NVyJ9cJIvd/e7FnXvn+SUTM4zBADW37VJntXdHxhO8Xh/VZ079J3S3b83w9oAmDPTHEqa7v5ikv+9TPvVmdy6AgDYAN19RYZTOLr7K1X18SR3nG1VAMyraW5wf9thj+HitkOr6o+r6rVV9Zi1Lw8AuCFVtS3JfZK8Z2h6RlV9uKpOq6oDVphnR1XtrKqdu3fv3qBKAdispjnH8CVJztgzUVXfkclVSZ+e5PgkZ1fVQ9e2PABgb4bP479K8ovDLaRenuSuSQ7LZI/i7y83X3ef2t3bu3v7wsLChtULwOY0TTB8YJJzFk0/MckdkhwzPH88ybPXrjQAYG+qar9MQuFru/sNSdLdV3b3dd39zSR/luTwWdYIwHyYJhgemOTSRdNHJ9nZ3W/r7s8lOT2Tw1gAgHU2XBH8VUk+3t1/sKj9oEXDHp/koo2uDYD5M83FZ/4jybctmn5YJmFwjy8lue0a1AQA3LAHJ3lSko9U1YVD268nOb6qDkvSSXYledpsygNgnkwTDD+V5Meq6k+S/HCS2yQ5b1H/wUm+uIa1AQAr6O4LktQyXecs0wYAezVNMPyTTPYQXp3k25Nckm8Nhg9J8pE1qwwAAIANMc0N7l9TVZ3kcUm+nOR3u/s/ksmtLDK5yf3L1qVKAAAA1s20N7j/n0n+5zLtX0jyg2tVFAAAABtnmquSAgAAsAVNtcewqvbN5FDS+yc5INcPlt3dT1mj2gAAANgAqw6GVXWbJO9Mcq9MroLW+c+rofWiNsEQAABgjkxzKOnvJLlHkqcmuWsmQfAxSb43yZlJ3hf3MQQAAJg70wTDxyZ5TXe/Osk1Q9t13f3J7v6pJF9P8qK1LhAAAID1NU0wvH0mewWT5Nrh+ZaL+t+U5EfWoigAAAA2zjTB8ItJbjW8/kqS/0hy8KL+/8jkgjQAAADMkWmC4aeS3DNJuvubST6Y5MSqukVVfXuSJye5ZO1LBAAAYD1NEwzfnuQJVXWLYfoPMrltxReTXJVke5JT1rY8AAAA1ts09zH83SS/193fSJLufl1VXZvkp5Jcl+T13X3WOtQIAADAOlp1MOzuTvKNJW1vSPKGtS4KAACAjTPNoaQAAABsQSvuMayqJ9+YBXb3a258OQAAAGy0vR1KenqSTlJTLK+TCIYAAABzZG/B8OEbVgUAAAAzs2Iw7O6/3chCAAAAmA0XnwEAABi5VQfDqnphVV20l/6PVNXz1qYsAAAANso0ewwfn+TcvfS/PckTblo5AAAAbLRpguGhST6xl/5PDmMAAACYI9OeY7j/XvoOSLLPTagFAACAGZgmGH40yXHLdVRVJfmR7H2PIgAAAJvQNMHwVUkeUFWnV9XCnsbh9WlJHjCMAQAAYI7s7Qb336K7/6yqHpbkyUmeVFVXDF0HJakkZ3X3y9ehRgAAANbRqoNhknT3T1XVW5L8ZJLvHprfl+S13f36tS4OAACA9TdVMEyS7n5dktetQy0AAADMwLRXJQUAAGCLEQwBAABGTjAEAAAYOcEQAABg5ARDAACAkVsxGFbVO6rqyEXTT66qbRtRFAAAABtnb3sMj0hy4KLpVyd50FquvKpOq6qrquqiRW23qapzq+rTw/MBK8x7wjDm01V1wlrWBQAAMCZ7C4ZXJDl00XStw/pPT3LUkrbnJDmvu++W5Lxh+ltU1W2SvCDJ/ZMcnuQFKwVIAAAA9m5vN7g/L8nzqmp7kquHth1V9ci9zNPd/ZTVrry737XM4anHZbK3MknOSHJ+kl9bMuYxSc7t7i8mSVWdm0nAPHO16wYAAGBib8Hwl5J0kkcmuf3w+qHDYyWdZNXBcAUHdvcVw+vP5VsPZ93jjkkuXTR92dB2PVW1I8mOJDnkkENuYmkAAABbz4qHknb3F7r7hO6+Y3fvk8mhpD/V3Tfby2OftSyuuzuTsHlTlnFqd2/v7u0LCwtrVBkAAMDWMc3tKl6Y5MPrVcgiV1bVQUkyPF+1zJjLkxy8aPpOQxsAAABTWnUw7O4Xdvfiq4ferqputw41vSXJnquMnpDkzcuM+Zskj66qA4aLzjx6aAMAAGBKU93gvqruUFVnVNWXklyZyd69q6vq9Kpa9hy/G1jemUneneTuVXVZVT0lyclJHlVVn87k/MaTh7Hbq+qVSTJcdOa3k7xvePzWngvRAAAAMJ29XXzmW1TVIUn+MZML0VyY5KND1z2TPDmTMPeA7r50hUVcT3cfv0LXkcuM3ZnkqYumT0ty2mrXBQAAwPJWHQwz2UN3QJJju/ucxR1VdXSSNwxjTlyz6gAAAFh30xxK+ugkL1saCpOku9+a5OW5/s3qAQAA2OSmCYYHJPn0Xvo/nWT/m1YOAAAAG22aYHhZkiP20v/QYQwAAABzZJpg+JdJ/mtVvaiqvmtPY1V9Z1X9bpIfT3LWWhcIAADA+pr24jMPSfJrSX6lqj47tN8hyT5J/j7J76xteQAAAKy3aW5w/6+ZHEr6tCRvT/K14fE3SXYkeXh3f30dagQAAGAdTbPHMN19bZI/Gx4AAABsAdOcYwgAAMAWJBgCAACMnGAIAAAwcoIhAADAyAmGAAAAI7eqYFhVt6iqh1bV3da7IADghh5KkqEAABfrSURBVFXVwVX1zqr6WFV9tKqeObTfpqrOrapPD88HzLpWADa/1e4xvC7JeUmOXsdaAIDVuzbJs7r7nkkekOTpVXXPJM9Jcl533y2Tz+7nzLBGAObEqoLhcP/CzyWp9S0HAFiN7r6iuz8wvP5Kko8nuWOS45KcMQw7I8njZlMhAPNkmnMM/zLJj1eV8xIBYBOpqm1J7pPkPUkO7O4rhq7PJTlwhXl2VNXOqtq5e/fuDakTgM1rmpD3yiTfnuTcqvrhqrpHVR2y9LFOdQIAy6iq70jyV0l+sbuvWdzX3Z2kl5uvu0/t7u3dvX1hYWEDKgVgM9t3irEXZfLhUkmO2Mu4fW5KQQDA6lTVfpmEwtd29xuG5iur6qDuvqKqDkpy1ewqBGBeTBMMfysrfOsIAGysqqokr0ry8e7+g0Vdb0lyQpKTh+c3z6A8AObMqoNhd5+0jnUAANN5cJInJflIVV04tP16JoHwdVX1lCT/nOTHZ1QfAHNkmj2GAMAm0d0XZOWrhR+5kbUAMP+musJoVd26qp5fVRcMN8594NB+u6H9HutTJgAAAOtl1XsMq2ohyQVJ7pLk4uH525Kkuz9fVSck2T/JL69DnWwS255z9oasZ9fJj92Q9QAAANMdSvo7SW6f5P5J/iXXv8rZm+PQFQAAgLkzzaGkxyZ5WXd/IMtfnfSSJAevSVUAAABsmGmC4e0yOYR0Jd9McsubVg4AAAAbbZpg+Lkkd91L/30yOcQUAACAOTJNMDwnyVOq6qClHVV1/yRPjpvoAgAAzJ1pguELk1yb5INJXpTJeYYnVNWZSd6V5LNJXrzmFQIAALCuVh0Mu/tzSR6Q5D1JfiaTm+o+KcmPJ3l7kod09xfXo0gAAADWzzS3q0h3X5rkuKr6ziR3zyQcXiwQAgAAzK+pguEe3X1NkvetcS0AAADMwNTBsKoOT/L4JHcZmi5J8qbufs9aFgYAAMDGWHUwrKp9kpya5MRMDiFd7NlV9ZokT+3u69auPAAAANbbNFclfV6Sn87klhQPSrL/8HhwkrdkcruK5611gQAAAKyvaYLhzyQ5t7t/tLv/sbuvGR7v7u7HJ3nHMAYAAIA5Mk0w/C+Z7BlcyZuGMQAAAMyRaYLhp5Lcfi/9Bw1jAAAAmCPTBMMXJXl6Vd17aUdV3SfJLyT53bUqDAAAgI2x4lVJq+r5yzR/JsnOqnp7kk8Mbd+b5FFJPpTke9a8QgAAANbV3m5XcdJe+o4eHovdN8l9kvz2TawJAACADbS3YHjohlUBAADAzKwYDLv7nzeyEAAAAGZjmovPAAAAsAXt7VDS66mqOyfZkeRuSW6bpJYM6e4+co1qAwAAYAOsOhhW1Y8k+csk+yW5JsnV61UUAAAAG2eaPYYvTnJpksd390fWqR4AAAA22DTnGG5L8kdCIQAAwNYyTTD8TJJbrFchAAAAzMY0wfAPkzy1qm61XsUAAACw8VZ9jmF3n1pV35nko1V1RpJdSa5bZtxr1q48AAAA1ts0VyU9MMmPJjkkyW+uMKyTCIYAAABzZJqrkr4iyf2SnJLk7+J2FQAAAFvCNMHwyCQv6e5fWa9iAAAA2HjTXHzmG0kuXq9CAAAAmI1pguHZSR61XoUAAAAwG9MEw19OcnBV/VFV3bWqar2KAgAAYONMc47h5zO56ugPJnl6kiyTDbu7p1kmAAAAMzZNiHtNJsEQAACALWSaG9yfuI51AAAAMCPTnGMIAADAFrTqPYZVdchqxnX3v9z4cgAAANho05xjuCurO8dwnxtXykRV3T3JWYua7pLk+d39h4vGHJHkzUk+MzS9obt/66asFwAAYKymCYa/lesHw32T3DXJcUk+kuStN7Wg7v5kksOSpKr2SXJ5kjcuM/TvuvvYm7o+AACAsZvm4jMnrdRXVXdJ8u4kO9egpsWOTPJP3f3Pa7xcAAAABmty8ZnuviTJnyZ54Vosb5GfSHLmCn0PrKoPVdVbq+r7VlpAVe2oqp1VtXP37t1rXB4AAMD8W8urkl6e5J5rtbCqunmSH0nyl8t0fyDJnbv73kn+OMmbVlpOd5/a3du7e/vCwsJalQcAALBlrGUwfFySq9dweUcn+UB3X7m0o7uv6e6vDq/PSbJfVd1uDdcNAAAwGtPcruL5K3TdJskjktwryX9fi6IGx2eFw0ir6vZJruzurqrDMwm4X1jDdQMAAIzGNFclPWkvfZ9L8rwkL75J1Qyq6lZJHpXkaYvafi5JuvsVSZ6Q5Oer6tokX0/yE929mltpAAAAsMQ0wfDQZdo6yRf3HNa5Vrr7a0luu6TtFYtevzTJS9dynQAAAGM1ze0q3DICAABgC1rLi88AAAAwh6Y5lDRV9cAkz0hyt0wO9awlQ7q777pGtQEAALABprkq6ZOTvDrJfyT5VJJ/Wa+iAAAA2DjT7DH8jSSfTPLI7v7sOtUDAADABpvmHMM7J3m5UAgAALC1TBMML0tyi/UqBAAAgNmYJhi+IslPVtU+61UMAAAAG2+acwzfn+THkry3qv4kyWeSXLd0UHe/a41qAwBWUFWnJTk2yVXdfa+h7aQkP5tk9zDs17v7nNlUCMA8mSYYnrfo9SuT9JL+GtrsUQSA9Xd6kpcmec2S9lO6+/c2vhwA5tk0wfCn160KAGAq3f2uqto26zoA2BpWHQy7+4z1LAQAWBPPGO49vDPJs7r76uUGVdWOJDuS5JBDDtnA8gDYjKa5+AwAsLm9PMldkxyW5Iokv7/SwO4+tbu3d/f2hYWFjaoPgE1KMASALaK7r+zu67r7m0n+LMnhs64JgPkgGALAFlFVBy2afHySi2ZVCwDzZZqLzwAAm0RVnZnkiCS3q6rLkrwgyRFVdVgmVwnfleRpMysQgLkiGALAHOru45dpftWGFwLAluBQUgAAgJETDAEAAEZOMAQAABg5wRAAAGDkBEMAAICREwwBAABGTjAEAAAYOcEQAABg5ARDAACAkRMMAQAARk4wBAAAGDnBEAAAYOQEQwAAgJETDAEAAEZOMAQAABg5wRAAAGDkBEMAAICREwwBAABGTjAEAAAYOcEQAABg5PaddQEAq7XtOWdvyHp2nfzYDVkPAMBmYY8hAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACMnGAIAAIycYAgAADBygiEAAMDICYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACMnGAIAAIycYAgAADBygiEAAMDICYYAAAAjJxgCAACMnGAIAAAwcoIhAADAyG3aYFhVu6rqI1V1YVXtXKa/quqPquriqvpwVd13FnUCAADMu31nXcANeHh3f36FvqOT3G143D/Jy4dnAAAAprBp9xiuwnFJXtMT/5hk/6o6aNZFAQAAzJvNvMewk7y9qjrJn3b3qUv675jk0kXTlw1tVyweVFU7kuxIkkMOOeQmF7XtOWff5GXckF0nP3bd1wEAALDHZt5j+EPdfd9MDhl9elU99MYspLtP7e7t3b19YWFhbSsEAADYAjZtMOzuy4fnq5K8McnhS4ZcnuTgRdN3GtoAAACYwqYMhlV1q6q69Z7XSR6d5KIlw96S5MnD1UkfkOTL3X1FAAAAmMpmPcfwwCRvrKpkUuOfd/fbqurnkqS7X5HknCTHJLk4yb8m+ekZ1QoAADDXNmUw7O5Lktx7mfZXLHrdSZ6+kXUBAABsRZvyUFIAAAA2jmAIAAAwcoIhAMypqjqtqq6qqosWtd2mqs6tqk8PzwfMskYA5oNgCADz6/QkRy1pe06S87r7bknOG6YBYK8EQwCYU939riRfXNJ8XJIzhtdnJHnchhYFwFwSDAFgazlw0X19P5fJLaCup6p2VNXOqtq5e/fujasOgE1JMASALWq4tVOv0Hdqd2/v7u0LCwsbXBkAm41gCABby5VVdVCSDM9XzbgeAOaAYAgAW8tbkpwwvD4hyZtnWAsAc0IwBIA5VVVnJnl3krtX1WVV9ZQkJyd5VFV9Oskjh2kA2Kt9Z10AAHDjdPfxK3QduaGFADD37DEEAAAYOcEQAABg5ARDAACAkRMMAQAARk4wBAAAGDnBEAAAYOQEQwAAgJETDAEAAEZOMAQAABg5wRAAAGDkBEMAAICREwwBAABGTjAEAAAYOcEQAABg5ARDAACAkRMMAQAARk4wBAAAGDnBEAAAYOQEQwAAgJETDAEAAEZOMAQAABg5wRAAAGDkBEMAAICREwwBAABGTjAEAAAYOcEQAABg5ARDAACAkRMMAQAARk4wBAAAGDnBEAAAYOQEQwAAgJETDAEAAEZOMAQAABg5wRAAAGDkBEMAAICREwwBAABGTjAEAAAYOcEQAABg5ARDAACAkRMMAQAARk4wBAAAGLl9Z10AzJNtzzl7Q9az6+THbsh6AAAgsccQAABg9ARDAACAkRMMAQAARk4wBAAAGDnBEAAAYOQEQwAAgJETDAEAAEZOMAQAABg5wRAAAGDkBEMAAICR23TBsKoOrqp3VtXHquqjVfXMZcYcUVVfrqoLh8fzZ1ErAADAVrDvrAtYxrVJntXdH6iqWyd5f1Wd290fWzLu77r72BnUBwAAsKVsuj2G3X1Fd39geP2VJB9PcsfZVgUAALB1bbpguFhVbUtynyTvWab7gVX1oap6a1V9316WsaOqdlbVzt27d69TpQAAAPNr0wbDqvqOJH+V5Be7+5ol3R9IcufuvneSP07yppWW092ndvf27t6+sLCwfgUDAADMqU0ZDKtqv0xC4Wu7+w1L+7v7mu7+6vD6nCT7VdXtNrhMANi0qmpXVX1kuEjbzlnXA8DmtukuPlNVleRVST7e3X+wwpjbJ7myu7uqDs8k4H5hA8sEgHnw8O7+/KyLAGDz23TBMMmDkzwpyUeq6sKh7deTHJIk3f2KJE9I8vNVdW2Sryf5ie7uWRQLAAAw7zZdMOzuC5LUDYx5aZKXbkxFADCXOsnbq6qT/Gl3n7q4s6p2JNmRJIcccsgMygNgM9mU5xgCADfZD3X3fZMcneTpVfXQxZ0uzgbAYoIhAGxB3X358HxVkjcmOXy2FQGwmQmGALDFVNWtqurWe14neXSSi2ZbFQCb2aY7xxAAuMkOTPLGyYW+s2+SP+/ut822JAA2M8EQALaY7r4kyb1nXQcA88OhpAAAACMnGAIAAIycYAgAADByzjGEObLtOWdvyHp2nfzYTbl+ZsvfHwC2LnsMAQAARk4wBAAAGDnBEAAAYOQEQwAAgJETDAEAAEZOMAQAABg5wRAAAGDkBEMAAICREwwBAABGTjAEAAAYOcEQAABg5ARDAACAkRMMAQAARk4wBAAAGDnBEAAAYOQEQwAAgJETDAEAAEZOMAQAABg5wRAAAGDkBEMAAICREwwBAABGTjAEAAAYOcEQAABg5ARDAACAkRMMAQAARk4wBAAAGDnBEAAAYOQEQwAAgJHbd9YFMJ1tzzl7Q9az6+THbsh6YJ7M+t/frNcPAGxd9hgCAACMnD2GAADATbIRR7U4omV92WMIAAAwcoIhAADAyAmGAAAAIycYAgAAjJxgCAAAMHKCIQAAwMgJhgAAACMnGALA/23vfkP1rOs4jr8/7Vg6CzU1MWdtoFhD8C/LsqRcxkxxERIKiYRlD2ZpBLF6EvVIIfrzQAJR2yj/ZFNpVPgHlXq2nNPQOVdz+Wc2nZVlJaSzbw/u68A584wGdX7Xta73Cw7nuu9zs8/3Pjvn/ux373dftyRJI+fCUJIkSZJGzoWhJEmSJI2cC0NJkiRJGjkXhpIkSZI0ci4MJUmSJGnkpvoeQJIk6b+1ePXPm+Q8dfV55u9FixnMH25+3/q+/33n/y/4P4aSJEmSNHIuDCVJkiRp5FwYSpIkSdLIuTCUJEmSpJFzYShJkiRJI+fCUJIkSZJGzoWhJEmSJI2cC0NJkiRJGjkXhpIkSZI0coNcGCZZkWRrkm1JVs/x9bck+XH39Q1JFrefUpKkYfpPPSpJ0p4GtzBMsgC4FjgXWApcnGTpHje7DHipqo4DvgNc03ZKSZKGaR97VJKkWQa3MASWAduqantVvQrcCqzc4zYrgbXd8TpgeZI0nFGSpKHalx6VJGmWVFXfM8yS5EJgRVV9trt8CfC+qrpixm0e626zo7v8ZHebP87x510OXN5dPAHYOs93YS5HAG+YzXzzzTff/Hnz7qo6sofc3u1Lj3bX24/mm2+++ePMn7Mjp/qYpKWqug64rs8ZkmysqtPNN998883XUNiP5ptvvvnjzZ/LELeSPgccO+Pyou66OW+TZAo4BPhTk+kkSRq2felRSZJmGeLC8EHg+CRLkrwZuAhYv8dt1gOXdscXAvfX0PbESpLUj33pUUmSZhncVtKq2p3kCuBuYAFwY1VtTvJNYGNVrQduAH6YZBvwZyalN2S9btUx33zzzR9x/ujsrUd7Hmtv+v75MN98880fa/4bDO7kM5IkSZKktoa4lVSSJEmS1JALQ0mSJEkaOReG8yjJiiRbk2xLsrqH/BuT7Ore97F19rFJHkjyeJLNSa5snH9gkl8n+U2X/42W+TPmWJDk4SQ/6yH7qSSPJnkkycYe8g9Nsi7JE0m2JHl/w+wTuvs9/fFykqta5XczfKn72XssyS1JDmycf2WXvbnVfZ/rMSfJ25Pcm+R33efDWsyi4euzI/vsxy7fjsSOtCPH05H7Sz+6MJwnSRYA1wLnAkuBi5MsbTzGGmBF48xpu4EvV9VS4AxgVeP7/0/g7Ko6CTgZWJHkjIb5064EtvSQO+0jVXVyT++T8z3grqp6D3ASDb8PVbW1u98nA6cBrwB3tspPcgzwReD0qjqRyQlAmp0kK8mJwOeAZUy+9+cnOa5B9Bre+JizGrivqo4H7usua+QG0JFr6K8fwY6cZkfakWPpyDXsB/3ownD+LAO2VdX2qnoVuBVY2XKAqvoVk7O2NldVO6tqU3f8NyYPeMc0zK+q+nt38YDuo+mZlpIsAs4Drm+ZOwRJDgHOYnIGYarq1ar6S0/jLAeerKqnG+dOAQdl8l6rC4E/NMx+L7Chql6pqt3AL4FPznfoXh5zVgJru+O1wCfmew7tF3rtyD77scu3I+1IO3JEHbm/9KMLw/lzDPDsjMs7aPigPyRJFgOnABsa5y5I8giwC7i3qprmA98FvgL8q3HutALuSfJQkssbZy8BXgR+0G0Tuj7JwY1nmHYRcEvLwKp6DvgW8AywE/hrVd3TcITHgA8lOTzJQuDjzH7D85aOqqqd3fHzwFE9zaFhsSM7dqQdaUeOtiMH148uDDWvkrwVuB24qqpebpldVa932yQWAcu6rQNNJDkf2FVVD7XKnMMHq+pUJlu1ViU5q2H2FHAq8P2qOgX4Bz1skcjkzb0vAH7SOPcwJs8ELgHeCRyc5NOt8qtqC3ANcA9wF/AI8Hqr/L2pyfsj+R5JUseOtCPtSDuym2kQ/ejCcP48x+xnHxZ1141GkgOYFN5NVXVHX3N02zMeoO3rSc4ELkjyFJMtUmcn+VHD/Oln5KiqXUxeO7CsYfwOYMeMZ6DXMSnB1s4FNlXVC41zPwr8vqperKrXgDuAD7QcoKpuqKrTquos4CXgty3zZ3ghydEA3eddPc2hYbEj7Ug70o4ce0cOrh9dGM6fB4HjkyzpnpG5CFjf80zNJAmTvfNbqurbPeQfmeTQ7vgg4BzgiVb5VfXVqlpUVYuZ/N3fX1XNng1LcnCSt00fAx9jsnWiiap6Hng2yQndVcuBx1vlz3AxjbfIdJ4BzkiysPtdWE7jEywkeUf3+V1MXjtxc8v8GdYDl3bHlwI/7WkODYsdaUfakXbk2DtycP041fcA/6+qaneSK4C7mZxt6caq2txyhiS3AB8GjkiyA/h6Vd3QKP5M4BLg0e41DABfq6pfNMo/GljbnfnuTcBtVdX8dNg9Ogq4c/J4yxRwc1Xd1XiGLwA3df/o2w58pmV4V/bnAJ9vmQtQVRuSrAM2MTn74MPAdY3HuD3J4cBrwKoWJzaY6zEHuBq4LcllwNPAp+Z7Dg1f3x3Zcz+CHdk3O9KObNqR+0s/ZrKlVZIkSZI0Vm4llSRJkqSRc2EoSZIkSSPnwlCSJEmSRs6FoSRJkiSNnAtDSZIkSRo5F4aSJEmSNHIuDCVJkiRp5P4NxIrsyCF+Q20AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "plt.title('Clustering', fontsize=20)\n",
    "info = cut_tree(hc_complete, NUM_CLUSTER).reshape(-1)\n",
    "ni = Counter(info)\n",
    "nh = list(ni.values())\n",
    "xpos = np.arange(NUM_CLUSTER)\n",
    "rects = plt.bar(xpos, nh, align='center')\n",
    "plt.xticks(xpos)\n",
    "plt.ylabel('number of class', fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title('Clustering-based filtering', fontsize=20)\n",
    "info = cut_tree(new_hc_complete, NUM_CLUSTER).reshape(-1)\n",
    "ni = Counter(info)\n",
    "nh = list(ni.values())\n",
    "xpos = np.arange(NUM_CLUSTER)\n",
    "rects = plt.bar(xpos, nh, align='center')\n",
    "plt.xticks(xpos)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the variance of distance between clusters are decreased and many stocks have turned to one cluster(0). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Where can we apply this?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As below, many pairs of distance decreased considerably and variance of distances decreased. So, we can use this in terms of correlation change. I believe this method might be a helpful to porfolio optimization because stock data are generally known to have a low signal to noise ratio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.4805914835281173, 'SNA', 'SO'),\n",
       " (0.48199496868945446, 'NOV', 'SO'),\n",
       " (0.4823168478523626, 'MAR', 'SO'),\n",
       " (0.48357925705486515, 'MU', 'SO'),\n",
       " (0.48372006069471096, 'CMI', 'SO')]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The biggest chagne in pairs.\n",
    "diff_store.sort()\n",
    "diff_store[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks for reading!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
